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Choose the option that best fits your workflow:\u003C/p\u003E\n","\u003Ch4\u003EOption 1: Snowsight UI (Recommended)\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest for:\u003C/strong\u003E Most users, visual learners, those new to Snowflake\u003C/p\u003E\n","\u003Cp\u003EDeploy directly from GitHub using Snowflake's built-in Git integration. This approach:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENo local setup required\u003C/strong\u003E - Everything runs in your browser\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVisual feedback\u003C/strong\u003E - Watch each step execute in Snowsight\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EEasy troubleshooting\u003C/strong\u003E - See errors immediately in the UI\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ELearn as you go\u003C/strong\u003E - Understand what each script does\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EYou'll connect Snowflake to GitHub, then run a series of SQL scripts that automatically:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EConfigure your account and create required objects\u003C/li\u003E\u003Cli\u003ELoad sample data from stages\u003C/li\u003E\u003Cli\u003EDeploy Cortex Search services and Analyst semantic models\u003C/li\u003E\u003Cli\u003ECreate notebooks for data processing\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EOption 2: Snowflake CoCo CLI (Alternative)\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBest for:\u003C/strong\u003E Developers, automation enthusiasts, CLI power users\u003C/p\u003E\n","\u003Cp\u003EUse the Telco Agent Builder skill for guided, conversational deployment. This approach:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EConversational interface\u003C/strong\u003E - AI guides you through deployment\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAutomated execution\u003C/strong\u003E - Less manual copying/pasting\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESkill-based\u003C/strong\u003E - Uses Snowflake CoCo's agent capabilities\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAssets Created\u003C/h3\u003E\n","\u003Cp\u003EBy the end of this lab, you'll have deployed:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: \u003Ccode\u003ETELCO_OPERATIONS_AI\u003C/code\u003E with multiple schemas\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWarehouse\u003C/strong\u003E: \u003Ccode\u003ETELCO_WH\u003C/code\u003E (Medium)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERole\u003C/strong\u003E: \u003Ccode\u003ETELCO_ANALYST_ROLE\u003C/code\u003E with CORTEX_USER privileges\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E14+ Tables\u003C/strong\u003E with ~10,000 rows of telco data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E2 Cortex Search Services\u003C/strong\u003E (call transcripts, support tickets)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E3 Cortex Analyst Semantic Models\u003C/strong\u003E (network, infrastructure, customer)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E1 Snowflake CoWork Agent\u003C/strong\u003E (Telco Operations AI Agent)\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E3 Snowflake Notebooks\u003C/strong\u003E for data processing\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E33+ Files\u003C/strong\u003E (25 MP3 audio, 8 PDF documents)\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EArchitecture Overview\u003C/h2\u003E\n","\u003Ch3\u003EMulti-Modal AI Platform\u003C/h3\u003E\n","\u003Cp\u003EThis quickstart deploys a \u003Cstrong\u003Ecomplete multi-modal AI platform\u003C/strong\u003E combining:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Sources\u003C/strong\u003E → \u003Cstrong\u003EAI Processing\u003C/strong\u003E → \u003Cstrong\u003EStructured Data\u003C/strong\u003E → \u003Cstrong\u003EAI Services\u003C/strong\u003E → \u003Cstrong\u003EApplications\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData Types\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E📄 Documents (PDFs - help guides, product information)\u003C/li\u003E\u003Cli\u003E🎙️ Audio (MP3 customer support calls)\u003C/li\u003E\u003Cli\u003E📊 Structured (Network metrics, customer data, 10,000+ rows)\u003C/li\u003E\u003Cli\u003E📧 Support Tickets (Customer issues and resolutions)\u003C/li\u003E\u003Cli\u003E📱 Customer Feedback (Sentiment analysis, complaints)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI Capabilities\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECortex Document Processing (AI_PARSE_DOCUMENT, AI_EXTRACT)\u003C/li\u003E\u003Cli\u003EAudio AI (AI_TRANSCRIBE with timestamps)\u003C/li\u003E\u003Cli\u003ESentiment AI (AI_SENTIMENT for call analysis)\u003C/li\u003E\u003Cli\u003ETranslation AI (AI_TRANSLATE for multi-language support)\u003C/li\u003E\u003Cli\u003EAggregation AI (AI_AGG without context limits)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELatest AISQL Syntax\u003C/strong\u003E: All examples use 2025 AI_* functions\u003C/p\u003E\n","\u003Ch3\u003ESystem Architecture\u003C/h3\u003E\n","\u003Cp\u003EThe NovaConnect AI platform follows a layered architecture that transforms raw unstructured data into actionable intelligence. Data flows from multiple sources (documents, audio, structured tables) through Cortex AI processing functions, into organized tables and search indices, and finally surfaces through Snowflake CoWork agents and applications.\u003C/p\u003E\n","\u003Cp\u003EThe architecture diagram below illustrates how each component connects:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ELeft side\u003C/strong\u003E: Raw data sources (PDFs, MP3s, CSVs) stored in Snowflake stages\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EMiddle\u003C/strong\u003E: AI processing layer using Cortex functions to extract, transcribe, and analyze content\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERight side\u003C/strong\u003E: Structured outputs (tables, search services, semantic models) that power the agent\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/architecture-overview.jpg\" alt=\"Telco Operations AI Architecture\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe dataflow diagram below shows how data moves through the system to power Snowflake CoWork. Raw data enters from the left (CSV files, PDF documents, MP3 audio recordings), gets processed through Cortex AI functions in the middle (AI_PARSE_DOCUMENT, AI_TRANSCRIBE, AI_SENTIMENT), and flows into the structured outputs on the right (Cortex Search services and Cortex Analyst semantic models). These services become the "tools" that the Snowflake CoWork agent uses to answer user questions—combining structured SQL queries with semantic search across unstructured content.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/architecture-diagram.png\" alt=\"Architecture Diagram\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EKey Technologies\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex AI Functions\u003C/strong\u003E: AI_PARSE_DOCUMENT, AI_TRANSCRIBE, AI_SENTIMENT, AI_COMPLETE\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E: 2 search services for semantic search and RAG\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E: 3 semantic models for natural language SQL\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E: Conversational agents with tool orchestration\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Document Processing\u003C/strong\u003E: Automated document processing at scale\u003C/li\u003E\u003C/ul\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESetup Your Environment\u003C/h2\u003E\n","\u003Ch3\u003EStep 1: Get a Snowflake Account\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption A - Free Trial\u003C/strong\u003E (Recommended):\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EVisit https://signup.snowflake.com/\u003C/li\u003E\u003Cli\u003ESign up for a free 30-day trial\u003C/li\u003E\u003Cli\u003EChoose \u003Cstrong\u003EEnterprise\u003C/strong\u003E edition\u003C/li\u003E\u003Cli\u003ESelect a cloud region (AWS, Azure, or GCP)\u003C/li\u003E\u003Cli\u003EVerify your email\u003C/li\u003E\u003Cli\u003ELog in to Snowsight (https://app.snowflake.com)\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EOption B - Existing Account\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EUse any Snowflake account with ACCOUNTADMIN access\u003C/li\u003E\u003Cli\u003ELog in to Snowsight\u003C/li\u003E\u003Cli\u003ENo special setup required\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EStep 2: Connect to GitHub Repository in Snowflake\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDeploy directly from GitHub - No downloads or CLI tools needed!\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2a: Create Git Integration (One-Time Setup)\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EIn \u003Cstrong\u003ESnowsight\u003C/strong\u003E, click on \u003Cstrong\u003EProjects\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EWorkspaces\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EAdd new \u003Cstrong\u003ESQL file\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ECopy and paste this script:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Setup Git Integration (one-time)\n-- This creates a SEPARATE database for Git repos so you can drop/recreate \n-- TELCO_OPERATIONS_AI without losing the Git integration\n\nUSE ROLE ACCOUNTADMIN;\n\n-- Create SEPARATE database for Git repositories (won't be dropped with main database)\nCREATE DATABASE IF NOT EXISTS TELCO_AI_LAB\n    COMMENT = 'Persistent database for Git repository integrations - DO NOT DROP';\nCREATE SCHEMA IF NOT EXISTS TELCO_AI_LAB.GIT_REPOS;\n\nUSE DATABASE TELCO_AI_LAB;\nUSE SCHEMA GIT_REPOS;\n\n-- Create API integration for GitHub\nCREATE OR REPLACE API INTEGRATION git_api_integration\n    API_PROVIDER = git_https_api\n    API_ALLOWED_PREFIXES = ('https://github.com/Snowflake-Labs/')\n    ENABLED = TRUE;\n\n-- Grant usage on API integration\nGRANT USAGE ON INTEGRATION git_api_integration TO ROLE ACCOUNTADMIN;\n\n-- Create Git repository object\nCREATE OR REPLACE GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO\n    API_INTEGRATION = git_api_integration\n    ORIGIN = 'https://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence.git';\n\n-- Grant READ permission on Git repository\nGRANT READ ON GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO TO ROLE ACCOUNTADMIN;\n\n-- Fetch code from GitHub\nALTER GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO FETCH;\n\nSELECT 'Git integration ready!' AS status,\n       'Git repo is in TELCO_AI_LAB database (separate from main database)' AS note;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ERun\u003C/strong\u003E (or press Cmd/Ctrl + Enter)\u003C/li\u003E\u003Cli\u003EWait for completion (~30 seconds)\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EGit integration complete!\u003C/strong\u003E You're now connected to GitHub\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EStep 2b: Access Git Repository in Snowflake UI\u003C/h4\u003E\n","\u003Cp\u003ENow will create a workspace from the github repository\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate a Workspace from Github repository by clicking on My Workspace, then select the option \u003Cstrong\u003EFrom Git repository\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EWhen Prompted, use the following URL in the \u003Cstrong\u003ERepository URL\u003C/strong\u003E field:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-text\"\u003Ehttps://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence.git\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003EPress \u003Cstrong\u003ECreate\u003C/strong\u003E\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENavigate to the assets folder\u003C/strong\u003E (at the root of the repository):\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYou should see the following file structure:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003Eassets/\n├── sql/                    ← Deployment scripts (START HERE)\n├── data/                   ← CSV files and PDFs\n├── audio/                  ← MP3 call recordings\n├── Notebooks/              ← Snowflake notebooks\n└── semantic_models/        ← YAML definitions\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"5\"\u003E\u003Cli\u003E\u003Cstrong\u003ENavigate to \u003Ccode\u003Esql/\u003C/code\u003E\u003C/strong\u003E - This is where the deployment scripts are\u003C/li\u003E\u003Cli\u003EYou'll see SQL files numbered 01-05\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E✅ \u003Cstrong\u003EYou're now ready to deploy!\u003C/strong\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep 3: Deploy from GitHub Using Git Integration\u003C/h3\u003E\n","\u003Ch4\u003EUnderstanding GitHub Integration in Snowflake\u003C/h4\u003E\n","\u003Cp\u003ESnowflake's \u003Cstrong\u003EGit Integration\u003C/strong\u003E feature allows you to connect directly to GitHub repositories and execute SQL scripts without downloading files locally. This is powerful because:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EVersion Control\u003C/strong\u003E: Scripts are always up-to-date from the source repository\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENo Downloads\u003C/strong\u003E: Execute directly from GitHub - no local files needed\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReproducible\u003C/strong\u003E: Same scripts, same results every time\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWorkspace Integration\u003C/strong\u003E: Browse, view, and edit files directly in Snowsight\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EWhen you created the Git Repository object in Step 2, Snowflake established a connection to the Telco AI GitHub repository. This connection allows you to:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003EBrowse files\u003C/strong\u003E in the repository through the Snowsight UI\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECreate Workspaces\u003C/strong\u003E from repository folders for interactive development\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFetch updates\u003C/strong\u003E when the repository changes\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe deployment scripts in \u003Ccode\u003Eassets/sql/\u003C/code\u003E are numbered 01-05 and should be executed in order. Each script builds on the previous one, creating the complete NovaConnect AI platform.\u003C/p\u003E\n","\u003Ch4\u003EDeploy Using the Git Repositories UI\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003ENavigate: \u003Cstrong\u003EProjects\u003C/strong\u003E → \u003Cstrong\u003EGit Repositories\u003C/strong\u003E → \u003Cstrong\u003ETELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EBrowse to: \u003Ccode\u003Eassets/sql/\u003C/code\u003E\u003C/li\u003E\u003Cli\u003ERight-click each file (01-05) → "Open in new worksheet"\u003C/li\u003E\u003Cli\u003EExecute each script in order\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat gets deployed\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E✅ Database \u003Ccode\u003ETELCO_OPERATIONS_AI\u003C/code\u003E with schemas\u003C/li\u003E\u003Cli\u003E✅ Role \u003Ccode\u003ETELCO_ANALYST_ROLE\u003C/code\u003E with CORTEX_USER privileges\u003C/li\u003E\u003Cli\u003E✅ 14+ tables with ~10,000 rows of data\u003C/li\u003E\u003Cli\u003E✅ 2 Cortex Search Services\u003C/li\u003E\u003Cli\u003E✅ 3 Cortex Analyst Semantic Models\u003C/li\u003E\u003Cli\u003E✅ 1 Snowflake CoWork Agent\u003C/li\u003E\u003Cli\u003E✅ 3 Notebooks\u003C/li\u003E\u003Cli\u003E✅ Stages with MP3 audio files and PDFs\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDeployment time\u003C/strong\u003E: 15-20 minutes\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EAlternative: Deploy with Snowflake CoCo CLI\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENew!\u003C/strong\u003E You can also deploy this quickstart using the \u003Cstrong\u003ESnowflake CoCo CLI\u003C/strong\u003E - Snowflake's AI-powered command-line assistant.\u003C/p\u003E\n","\u003Ch4\u003EOption C: Automated Deployment with Snowflake CoCo\u003C/h4\u003E\n","\u003Cp\u003EIf you have Snowflake CoCo CLI installed, you can use the built-in \u003Cstrong\u003Eskills\u003C/strong\u003E for guided, automated deployment. Skills are structured markdown instructions that guide Snowflake CoCo through complex deployment procedures.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. Clone the Repository\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence.git\ncd sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Configure Your Snowflake Connection\u003C/strong\u003E (if not already configured)\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# List available connections\nsnow connection list\n\n# Or create a new connection\nsnow connection add\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Register the Project Skills\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThe repository includes custom skills in \u003Ccode\u003E.cortex/skills/\u003C/code\u003E, but they need to be registered before use. Run this command once to add the skills directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Register the skills directory (one-time setup)\ncortex skill add .cortex/skills\n\n# Verify the skills were added\ncortex skill list\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see output like:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAdded skill directory: /path/to/.cortex/skills\nSkills found: telco-agent-builder, telco-agent-uninstall\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Skills require a file named \u003Ccode\u003ESKILL.md\u003C/code\u003E (uppercase) in each skill subdirectory. The repository is pre-configured with this structure.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. Launch Snowflake CoCo\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EStart Snowflake CoCo from the project directory:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5. Deploy Using the Skill\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce Snowflake CoCo is running, simply ask it to deploy. Use any of these prompts:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E> Deploy the NovaConnect Telco Operations AI quickstart\n> Build the telco agent\n> Set up the telecommunications agent\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ESnowflake CoCo will load the \u003Ccode\u003Etelco-agent-builder\u003C/code\u003E skill and guide you through each step interactively.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003E6. (Optional) Customize Regions Before Deployment\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThe skill will ask if you want to customize the demo regions. By default, the data uses Malaysian regions (Kuala Lumpur, Selangor, Penang, etc.).\u003C/p\u003E\n","\u003Cp\u003EIf you prefer different regions, the skill will run a Python script to generate customized CSV files:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# US Cities preset\npython assets/scripts/generate_regional_data.py --preset us\n\n# European Cities preset\npython assets/scripts/generate_regional_data.py --preset european\n\n# Generic regions (Region Alpha, Region Beta, etc.)\npython assets/scripts/generate_regional_data.py --preset generic\n\n# Custom mapping\npython assets/scripts/generate_regional_data.py --mapping "Kuala Lumpur:Sydney,Selangor:Melbourne"\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis generates new CSV files in \u003Ccode\u003Eassets/data/regional/\u003C/code\u003E which the skill will upload instead of the defaults.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch4\u003EAvailable Skills\u003C/h4\u003E\n","\u003Cp\u003EThe repository includes two skills in \u003Ccode\u003E.cortex/skills/\u003C/code\u003E:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESkill\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDescription\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETrigger Phrases\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003Etelco-agent-builder\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDeploy the complete solution\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"deploy telco", "build the agent", "set up novaconnect"\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003Etelco-agent-uninstall\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClean up all resources\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E"uninstall telco", "remove the agent", "cleanup"\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Chr\u003E\n","\u003Ch4\u003EWhat the Deployment Skill Does\u003C/h4\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003Etelco-agent-builder\u003C/strong\u003E skill guides you through these steps:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E✅ \u003Cstrong\u003EVerify prerequisites\u003C/strong\u003E - Check Snowflake connection and permissions\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ECustomize regions\u003C/strong\u003E (optional) - Generate regional data if requested\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EConfigure account\u003C/strong\u003E - Create role, warehouse, database, schemas, and stages\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EUpload data\u003C/strong\u003E - Copy CSV, PDF, and MP3 files to Snowflake stages\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ELoad tables\u003C/strong\u003E - Create and populate 14+ tables with telco data\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EDeploy Cortex Search\u003C/strong\u003E - Create 2 semantic search services\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EDeploy Cortex Analyst\u003C/strong\u003E - Upload semantic models and create the Intelligence Agent\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EDeploy Notebooks\u003C/strong\u003E - Create 3 Snowflake Notebooks\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EVerify deployment\u003C/strong\u003E - Run checks to confirm everything is working\u003C/li\u003E\u003C/ol\u003E\n\u003Chr\u003E\n","\u003Ch4\u003EUninstalling with Snowflake CoCo\u003C/h4\u003E\n","\u003Cp\u003ETo remove all deployed resources, use the uninstall skill:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E> Uninstall the telco agent\n> Clean up the NovaConnect deployment\n> Remove the telco quickstart\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis will drop the database, warehouse, role, and all related objects.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch4\u003EBenefits of CLI Deployment\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EInteractive guidance\u003C/strong\u003E - Step-by-step with explanations at each stage\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EError handling\u003C/strong\u003E - Automatic troubleshooting and suggested fixes\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EProgress tracking\u003C/strong\u003E - Real-time status updates\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustomization\u003C/strong\u003E - Easily modify regions, settings, or skip optional steps\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EIdempotent\u003C/strong\u003E - Safe to re-run if interrupted\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch4\u003ETroubleshooting Skills\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESkills not found?\u003C/strong\u003E If Snowflake CoCo doesn't recognize the skills, verify they're registered:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003E# Check registered skills\ncortex skill list\n\n# If not listed, add the skills directory\ncortex skill add .cortex/skills\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003E"No valid skills found" error?\u003C/strong\u003E Skills require a specific file structure:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EEach skill must be in its own subdirectory (e.g., \u003Ccode\u003E.cortex/skills/telco-agent-builder/\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003EThe skill file must be named \u003Ccode\u003ESKILL.md\u003C/code\u003E (uppercase, not \u003Ccode\u003Eskill.md\u003C/code\u003E or \u003Ccode\u003E<name>.md\u003C/code\u003E)\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETo remove a registered skill directory:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Ecortex skill remove .cortex/skills\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote\u003C/strong\u003E: Snowflake CoCo CLI is currently in Private Preview. Contact your Snowflake account team to request access.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EStep 4: Verify Deployment\u003C/h3\u003E\n","\u003Cp\u003EAfter deployment completes, verify in Snowflake UI:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check all components\nUSE DATABASE TELCO_OPERATIONS_AI;\n\nSHOW TABLES IN SCHEMA DEFAULT_SCHEMA;              -- Should see 14+ tables\nSHOW CORTEX SEARCH SERVICES IN SCHEMA DEFAULT_SCHEMA; -- Should see 2 services\nSHOW NOTEBOOKS IN SCHEMA NOTEBOOKS;                -- Should see 3 notebooks\nSHOW AGENTS IN SCHEMA SNOWFLAKE_INTELLIGENCE.AGENTS;  -- Should see Telco Operations AI Agent\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAll set?\u003C/strong\u003E ✅ Continue to the next section!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVerify Your Deployment\u003C/h2\u003E\n","\u003Cp\u003EAfter Git integration deployment completes, let's verify everything was created successfully.\u003C/p\u003E\n","\u003Ch3\u003ECheck Database Objects\u003C/h3\u003E\n","\u003Cp\u003EOpen Snowflake UI and navigate to \u003Cstrong\u003EData\u003C/strong\u003E → \u003Cstrong\u003EDatabases\u003C/strong\u003E → \u003Cstrong\u003ETELCO_OPERATIONS_AI\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EYou should see:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E✅ \u003Cstrong\u003EDEFAULT_SCHEMA\u003C/strong\u003E - Main data tables\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ECORTEX_ANALYST\u003C/strong\u003E - Semantic model stage and views\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ENOTEBOOKS\u003C/strong\u003E - Snowflake notebooks\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003ESTREAMLIT\u003C/strong\u003E - Streamlit applications schema\u003C/li\u003E\u003Cli\u003E✅ \u003Cstrong\u003EMODELS\u003C/strong\u003E - ML models and UDFs schema\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVerify Tables (14+)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EUSE DATABASE TELCO_OPERATIONS_AI;\nUSE SCHEMA DEFAULT_SCHEMA;\n\n-- Show all tables\nSHOW TABLES;\n\n-- Verify key table counts\nSELECT 'network_performance' AS table_name, COUNT(*) AS rows FROM network_performance\nUNION ALL SELECT 'infrastructure_capacity', COUNT(*) FROM infrastructure_capacity\nUNION ALL SELECT 'customer_details', COUNT(*) FROM customer_details\nUNION ALL SELECT 'customer_feedback_summary', COUNT(*) FROM customer_feedback_summary\nUNION ALL SELECT 'CALL_TRANSCRIPTS', COUNT(*) FROM CALL_TRANSCRIPTS\nUNION ALL SELECT 'AGENT_PERFORMANCE', COUNT(*) FROM AGENT_PERFORMANCE\nUNION ALL SELECT 'NETWORK_INCIDENTS', COUNT(*) FROM NETWORK_INCIDENTS;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerify Cortex Search Services (2)\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW CORTEX SEARCH SERVICES IN SCHEMA DEFAULT_SCHEMA;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E✅ CALL_TRANSCRIPT_SEARCH\u003C/li\u003E\u003Cli\u003E✅ SUPPORT_TICKET_SEARCH\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EVerify Applications\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENotebooks\u003C/strong\u003E:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW NOTEBOOKS IN TELCO_OPERATIONS_AI.NOTEBOOKS;\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cul\u003E\u003Cli\u003E✅ 1_DATA_PROCESSING\u003C/li\u003E\u003Cli\u003E✅ 2_ANALYZE_CALL_AUDIO\u003C/li\u003E\u003Cli\u003E✅ 3_INTELLIGENCE_LAB\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAll verified?\u003C/strong\u003E ✅ Let's start using the AI features!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EExplore the Demo\u003C/h2\u003E\n","\u003Cp\u003EDuration: 45\u003C/p\u003E\n","\u003Ch3\u003EInstallation Complete - Now Let's Explore!\u003C/h3\u003E\n","\u003Cp\u003ECongratulations! You've successfully deployed the NovaConnect Telco Operations AI platform. The installation phase is complete.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat you've deployed:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EDatabase with 14+ tables of telco data\u003C/li\u003E\u003Cli\u003E2 Cortex Search services for semantic search\u003C/li\u003E\u003Cli\u003E3 Cortex Analyst semantic models for natural language queries\u003C/li\u003E\u003Cli\u003E1 Snowflake CoWork Agent\u003C/li\u003E\u003Cli\u003E3 Snowflake Notebooks for data processing\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat's next:\u003C/strong\u003E\nIn the following sections, you'll explore and interact with the AI capabilities you just deployed:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EStage\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESection\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EWhat You'll Do\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E1\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExplore Cortex Playground and Document Processing Playground to understand LLM capabilities\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E2\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003ESnowflake Notebooks\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERun 3 notebooks to process documents, transcribe audio, and explore AI functions\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E- Document Processing\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EProcess PDFs with AI_PARSE_DOCUMENT (Notebook 1)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E- Audio Analysis\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETranscribe call recordings with AI_TRANSCRIBE (Notebook 2)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E \u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E- Intelligence Lab\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAdvanced AI exploration and analytics (Notebook 3)\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E3\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EAI Services\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EExplore the deployed Cortex Search and Cortex Analyst services\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003E4\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EIntelligence Agent\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EChat with your telco data using the deployed agent\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EAI & ML Studio\u003C/h2\u003E\n","\u003Ch3\u003ESnowflake AI and ML Studio\u003C/h3\u003E\n","\u003Cp\u003EBefore diving into the notebooks, let's explore the \u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E - your one-stop shop for trying out AI functions using a user-friendly UI.\u003C/p\u003E\n","\u003Cp\u003ENavigate to the \u003Cstrong\u003EAI & ML\u003C/strong\u003E section in the Snowflake navigation bar.\u003C/p\u003E\n","\u003Ch3\u003EFeatures You'll Explore\u003C/h3\u003E\n","\u003Cp\u003EThe AI and ML Studio provides access to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Playground\u003C/strong\u003E - Compare text completions across multiple LLMs\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Fine Tuning\u003C/strong\u003E - Customize large language models for specific tasks\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Search\u003C/strong\u003E - Low-latency semantic search over your data\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E - Text-to-SQL for business intelligence\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDocument Processing Playground\u003C/strong\u003E - Explore AI_EXTRACT and AI_PARSE_DOCUMENT functions\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ECortex Playground\u003C/h3\u003E\n","\u003Cp\u003EThe Cortex LLM Playground lets you compare text completions across multiple large language models available in Cortex AI.\u003C/p\u003E\n","\u003Ch4\u003EWhy This Matters for Unstructured Data\u003C/h4\u003E\n","\u003Cp\u003EBefore processing unstructured documents at scale, it's valuable to understand how LLMs interpret and extract information from text. The Cortex Playground provides a sandbox to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ETest different models\u003C/strong\u003E - Compare how Claude, Llama, Mistral, and other models handle your specific use cases\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERefine your prompts\u003C/strong\u003E - Experiment with prompt engineering before building production pipelines\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EUnderstand model capabilities\u003C/strong\u003E - See how models extract key information, summarize content, and answer questions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EValidate extraction strategies\u003C/strong\u003E - Test whether a model can reliably pull specific data points from unstructured text\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis foundational understanding will help you design better document processing workflows when you move to the Notebooks section.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/cortex-playground.png\" alt=\"Cortex Playground\"\u003E\u003C/p\u003E\n","\u003Ch4\u003ETry it now:\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EClick on \u003Cstrong\u003ECortex Playground\u003C/strong\u003E in the AI & ML Studio\u003C/li\u003E\u003Cli\u003ESelect a model (e.g., \u003Ccode\u003Eclaude-4-sonnet\u003C/code\u003E, \u003Ccode\u003Ellama-3.1-70b\u003C/code\u003E, \u003Ccode\u003Emistral-large2\u003C/code\u003E)\u003C/li\u003E\u003Cli\u003ETry asking a telecommunications question:\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample prompt:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"What are the key metrics I should monitor for 5G network performance? What factors indicate potential service degradation?"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat you'll see:\u003C/strong\u003E\nThe model will suggest various factors to consider:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENetwork latency and jitter\u003C/li\u003E\u003Cli\u003EDownload/upload speeds\u003C/li\u003E\u003Cli\u003EPacket loss rates\u003C/li\u003E\u003Cli\u003EHandover success rates\u003C/li\u003E\u003Cli\u003ESignal strength (dBm)\u003C/li\u003E\u003Cli\u003EUser load and capacity utilization\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EExperiment with Document-Style Prompts\u003C/h4\u003E\n","\u003Cp\u003ETry pasting a sample of unstructured text and asking the model to extract specific information:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample - Extracting from a support ticket:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Extract the following from this support ticket: customer issue, product mentioned, urgency level, and resolution requested.\u003C/p\u003E\n","\u003Cp\u003ETicket: Customer called regarding slow internet speeds on their NovaConnect Pro plan. They've been experiencing issues for 3 days and have already tried restarting their router. Customer is frustrated as they work from home and need this resolved urgently. Requesting a technician visit or account credit."\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat you'll learn:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EHow well the model identifies key entities\u003C/li\u003E\u003Cli\u003EWhether it can handle domain-specific terminology\u003C/li\u003E\u003Cli\u003EHow to structure extraction prompts for consistent results\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey insight\u003C/strong\u003E: The prompt engineering skills you develop here translate directly to the AI_EXTRACT and AI_PARSE_DOCUMENT functions you'll use in the Notebooks.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EDocument Processing Playground\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/document-processing-playground.png\" alt=\"Document Processing Playground\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EDocument Processing Playground\u003C/strong\u003E is a powerful AI tool that helps you understand how text is extracted from documents. It provides an interactive UI for testing \u003Cstrong\u003EAI_EXTRACT\u003C/strong\u003E and \u003Cstrong\u003EAI_PARSE_DOCUMENT\u003C/strong\u003E functions, allowing you to experiment with different extraction strategies before implementing them in production.\u003C/p\u003E\n","\u003Ch4\u003EStep 1: Upload Documents from Stage\u003C/h4\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003EDocument Processing Playground\u003C/strong\u003E in the AI & ML Studio\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EAdd from stage\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect the following:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase\u003C/strong\u003E: \u003Ccode\u003ETELCO_OPERATIONS_AI\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema\u003C/strong\u003E: \u003Ccode\u003EDEFAULT_SCHEMA\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EStage\u003C/strong\u003E: \u003Ccode\u003EPDF_STAGE\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003EChoose 1-2 PDF documents (e.g., NovaConnect help documents)\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EOpen playground\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/pdfs_document_playground.png\" alt=\"Document Selected in Playground\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Extract Information Using Questions\u003C/h4\u003E\n","\u003Cp\u003EOnce your document is loaded, you'll see three tabs: \u003Cstrong\u003EExtraction\u003C/strong\u003E, \u003Cstrong\u003EMarkdown\u003C/strong\u003E, and \u003Cstrong\u003EText\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003EExtraction\u003C/strong\u003E tab is where you can ask questions to pull specific information from the document.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry creating these key-value question pairs:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EKey\u003C/strong\u003E: \u003Ccode\u003Eproduct_name\u003C/code\u003E \u003Cstrong\u003EQuestion\u003C/strong\u003E: \u003Ccode\u003EWhat is the name of the product or service?\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKey\u003C/strong\u003E: \u003Ccode\u003Efeatures\u003C/code\u003E \u003Cstrong\u003EQuestion\u003C/strong\u003E: \u003Ccode\u003EWhat are the key features mentioned?\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKey\u003C/strong\u003E: \u003Ccode\u003Eprice\u003C/code\u003E \u003Cstrong\u003EQuestion\u003C/strong\u003E: \u003Ccode\u003EWhat is the pricing information?\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKey\u003C/strong\u003E: \u003Ccode\u003Edata_allowance\u003C/code\u003E \u003Cstrong\u003EQuestion\u003C/strong\u003E: \u003Ccode\u003EWhat is the data allowance or quota?\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EKey\u003C/strong\u003E: \u003Ccode\u003Econtract_terms\u003C/code\u003E \u003Cstrong\u003EQuestion\u003C/strong\u003E: \u003Ccode\u003EWhat are the contract terms?\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAfter entering each question, click \u003Cstrong\u003EAdd Prompt\u003C/strong\u003E to see the extracted results.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/contract-terms.png\" alt=\"Extraction with Contract Terms\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Get the SQL Code\u003C/h4\u003E\n","\u003Cp\u003EOnce you've asked at least one question, the playground automatically generates SQL code:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003ECode Snippets\u003C/strong\u003E in the top right corner\u003C/li\u003E\u003Cli\u003EReview the generated SQL using AI_EXTRACT and AI_PARSE_DOCUMENT functions\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EWorkspaces\u003C/strong\u003E to open the code in a new worksheet\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample generated code:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Extract Data\nSELECT AI_EXTRACT(\n    file => TO_FILE('@TELCO_OPERATIONS_AI.DEFAULT_SCHEMA.PDF_STAGE', 'NovaConnect_Easy_360.pdf'),\n    responseFormat => PARSE_JSON('{"schema":{"type":"object","properties":{"contract_terms":{"description":"what are the contract terms","type":"string"}}}}')\n) AS extracted_data;\n\n-- Parse file (Layout mode)\nSELECT AI_PARSE_DOCUMENT(\n    TO_FILE('@TELCO_OPERATIONS_AI.DEFAULT_SCHEMA.PDF_STAGE', 'NovaConnect_Easy_360.pdf'),\n    { 'mode': 'LAYOUT', 'page_split': true }\n) AS parsed_document;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis SQL code can be used to automate document processing at scale!\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E💡 Tip:\u003C/strong\u003E You'll see these functions in action in the \u003Cstrong\u003E1_DATA_PROCESSING\u003C/strong\u003E notebook, which processes all 8 NovaConnect PDF documents using \u003Ccode\u003EAI_PARSE_DOCUMENT\u003C/code\u003E with LAYOUT mode for bulk text extraction.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESummary: What You've Learned\u003C/h3\u003E\n","\u003Cp\u003EBefore proceeding to the notebooks, you now understand:\u003C/p\u003E\n","\u003Cp\u003E✅ \u003Cstrong\u003ECortex Playground\u003C/strong\u003E - How to test LLM capabilities and prompt engineering\n✅ \u003Cstrong\u003EAI_PARSE_DOCUMENT\u003C/strong\u003E - Extract text and structure from PDFs\n✅ \u003Cstrong\u003EAI_EXTRACT\u003C/strong\u003E - Pull specific fields from documents using questions\n✅ \u003Cstrong\u003ESQL Code Generation\u003C/strong\u003E - Automating document processing at scale\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENext\u003C/strong\u003E: Apply these concepts in the notebooks to process help documents and call recordings!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EDocument Processing\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this section, you'll process unstructured documents using Cortex AI functions through Notebook 1.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Process:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E📄 \u003Cstrong\u003E8 Help Documents\u003C/strong\u003E (PDFs) - Product guides, troubleshooting documents, and service information\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex AI Functions You'll Use:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAI_PARSE_DOCUMENT\u003C/strong\u003E - Extract all text from PDFs preserving layout\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI_COMPLETE\u003C/strong\u003E - Generate structured extractions using LLMs\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAI_SENTIMENT\u003C/strong\u003E - Analyze emotional tone in customer communications\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EOpen Notebook 1\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E → \u003Cstrong\u003ENotebooks\u003C/strong\u003E → \u003Cstrong\u003E1_DATA_PROCESSING\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat the Notebook Demonstrates\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPDF Processing\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EParse help documents with AI_PARSE_DOCUMENT\u003C/li\u003E\u003Cli\u003EExtract product features, pricing, and data allowances\u003C/li\u003E\u003Cli\u003ECreate searchable content from unstructured PDFs\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Tables Created by This Notebook\u003C/strong\u003E:\u003C/p\u003E\n","\u003Cp\u003EAfter running this notebook, you'll have processed PDFs ready for search and analysis.\u003C/p\u003E\n","\u003Ch3\u003EFollow the Notebook\u003C/h3\u003E\n","\u003Cp\u003EThe notebook contains \u003Cstrong\u003Edetailed instructions and explanations\u003C/strong\u003E for each step. Simply:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EClick \u003Cstrong\u003EStart\u003C/strong\u003E to begin the notebook session\u003C/li\u003E\u003Cli\u003ERead through each cell's markdown explanations\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERun All\u003C/strong\u003E or execute cells one by one\u003C/li\u003E\u003Cli\u003EObserve the outputs and interactive visualizations\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime\u003C/strong\u003E: 10-15 minutes to run through all cells.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EAudio Analysis\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EProcess customer call recordings using \u003Cstrong\u003EAI_TRANSCRIBE\u003C/strong\u003E to convert speech to text, then analyze the content for sentiment and insights.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat You'll Process:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E🎙️ \u003Cstrong\u003E25 Customer Support Calls\u003C/strong\u003E (MP3 audio files)\u003C/li\u003E\u003Cli\u003E🗣️ \u003Cstrong\u003ESpeaker Identification\u003C/strong\u003E - Separate customer and agent comments\u003C/li\u003E\u003Cli\u003E💭 \u003Cstrong\u003ESentiment Analysis\u003C/strong\u003E - Measure emotional tone throughout the call\u003C/li\u003E\u003Cli\u003E📝 \u003Cstrong\u003ECall Summarization\u003C/strong\u003E - Generate AI summaries of each call\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EOpen Notebook 2\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E → \u003Cstrong\u003ENotebooks\u003C/strong\u003E → \u003Cstrong\u003E2_ANALYZE_CALL_AUDIO\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat the Notebook Demonstrates\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI_TRANSCRIBE\u003C/strong\u003E - Converts MP3 audio to timestamped text:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EGenerates full transcripts with speaker identification\u003C/li\u003E\u003Cli\u003EPreserves timestamps for navigation\u003C/li\u003E\u003Cli\u003EProcesses multi-speaker conversations\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI_SENTIMENT\u003C/strong\u003E - Analyzes emotional tone:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EScores sentiment segment by segment\u003C/li\u003E\u003Cli\u003EIdentifies positive, negative, and neutral sections\u003C/li\u003E\u003Cli\u003ETracks sentiment trends over the call\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAI_COMPLETE\u003C/strong\u003E - Summarizes calls:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECreates concise call summaries\u003C/li\u003E\u003Cli\u003EIdentifies key issues discussed\u003C/li\u003E\u003Cli\u003EExtracts action items and resolutions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EKey Tables Updated by This Notebook\u003C/h3\u003E\n","\u003Cp\u003EAfter running this notebook, you'll have:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECUSTOMER_CALL_TRANSCRIPTS\u003C/strong\u003E - Full transcripts with summaries\u003C/li\u003E\u003Cli\u003EUpdated sentiment analysis for calls\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E💡 \u003Cstrong\u003ENote\u003C/strong\u003E: The \u003Ccode\u003ECALL_TRANSCRIPTS\u003C/code\u003E table with sample data is pre-loaded during initial deployment for the search services to work immediately.\u003C/p\u003E\n","\u003Ch3\u003EFollow the Notebook\u003C/h3\u003E\n","\u003Cp\u003EThe notebook includes:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAudio file listings and metadata\u003C/li\u003E\u003Cli\u003ETranscription with AI_TRANSCRIBE\u003C/li\u003E\u003Cli\u003ESentiment analysis with AI_SENTIMENT\u003C/li\u003E\u003Cli\u003ECall summarization with AI_COMPLETE\u003C/li\u003E\u003Cli\u003EInteractive visualizations\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ESimply run through the cells to see how audio becomes searchable, analyzable data.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime\u003C/strong\u003E: 10-15 minutes to complete.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIntelligence Lab\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003ENotebook 3 provides advanced analytics and visualizations combining all your processed data.\u003C/p\u003E\n","\u003Ch3\u003EOpen Notebook 3\u003C/h3\u003E\n","\u003Cp\u003ENavigate to \u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E → \u003Cstrong\u003ENotebooks\u003C/strong\u003E → \u003Cstrong\u003E3_INTELLIGENCE_LAB\u003C/strong\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat the Notebook Demonstrates\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECross-Domain Analysis\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECorrelating network issues with customer complaints\u003C/li\u003E\u003Cli\u003EIdentifying patterns in customer churn\u003C/li\u003E\u003Cli\u003ERegional performance comparisons\u003C/li\u003E\u003Cli\u003EAgent performance analytics\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EVisualizations\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENetwork performance dashboards\u003C/li\u003E\u003Cli\u003ECustomer sentiment trends\u003C/li\u003E\u003Cli\u003ECapacity utilization charts\u003C/li\u003E\u003Cli\u003ECall center metrics\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EFollow the Notebook\u003C/h3\u003E\n","\u003Cp\u003EThis notebook brings together insights from all data sources for comprehensive operational intelligence.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETime\u003C/strong\u003E: 10-15 minutes to complete.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECortex Search Services\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EThe deployment scripts created \u003Cstrong\u003E2 intelligent search services\u003C/strong\u003E that make your telecommunications data instantly accessible using semantic search. This is the foundation for RAG (Retrieval Augmented Generation) applications.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhy Search Services Matter:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETraditional keyword search fails with conversational content\u003C/li\u003E\u003Cli\u003ESemantic search understands meaning and context\u003C/li\u003E\u003Cli\u003ECritical for AI agents to find relevant information quickly\u003C/li\u003E\u003Cli\u003EPowers the Telco Operations agent you'll use later\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat's Deployed:\u003C/strong\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ECALL_TRANSCRIPT_SEARCH\u003C/strong\u003E - Search customer call transcripts by conversation content\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESUPPORT_TICKET_SEARCH\u003C/strong\u003E - Search support tickets by issue description\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ETest the Search Services\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E → \u003Cstrong\u003ECortex Search\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003ECALL_TRANSCRIPT_SEARCH\u003C/strong\u003E service\u003C/li\u003E\u003Cli\u003ETry a search: "network connectivity problems"\u003C/li\u003E\u003Cli\u003EObserve:\n\u003Cul\u003E\u003Cli\u003ESemantic search results (not exact match)\u003C/li\u003E\u003Cli\u003ERanked by relevance\u003C/li\u003E\u003Cli\u003EAttributes returned (CALL_ID, SPEAKER_ROLE, SENTIMENT_SCORE)\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESearch Service Details\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EService\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESearch Column\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EAttributes\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECALL_TRANSCRIPT_SEARCH\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECustomer call transcripts\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESEGMENT_TEXT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECALL_ID, SPEAKER_ROLE, SENTIMENT_SCORE, CALL_TIMESTAMP\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESUPPORT_TICKET_SEARCH\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESupport tickets\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EDESCRIPTION\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETICKET_ID, CUSTOMER_ID, CATEGORY, STATUS, PRIORITY\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECortex Analyst\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex Analyst\u003C/strong\u003E enables natural language querying of structured data using semantic models. Instead of writing SQL, users ask questions in plain English and Cortex Analyst generates the SQL automatically.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat's Deployed:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E📊 \u003Cstrong\u003E3 Semantic Models\u003C/strong\u003E - Network performance, infrastructure capacity, customer feedback\u003C/li\u003E\u003Cli\u003E🔍 \u003Cstrong\u003ENatural Language to SQL\u003C/strong\u003E - Ask questions, get SQL and results\u003C/li\u003E\u003Cli\u003E📈 \u003Cstrong\u003EVerified Queries\u003C/strong\u003E - Pre-tested queries for common questions\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ESemantic Model 1: Network Performance\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Query network metrics across cell towers and regions\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Dimensions\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETOWER_ID, TOWER_NAME, REGION, NETWORK_TYPE\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Facts\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAVG_LATENCY_MS, AVG_DOWNLOAD_SPEED_MBPS, PACKET_LOSS_PCT\u003C/li\u003E\u003Cli\u003ECALL_DROP_RATE_PCT, HANDOVER_SUCCESS_RATE_PCT, AVAILABILITY_PCT\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample Questions\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E"Which towers have high latency issues?"\u003C/li\u003E\u003Cli\u003E"What are the best performing 5G towers by download speed?"\u003C/li\u003E\u003Cli\u003E"Show me network availability by region"\u003C/li\u003E\u003Cli\u003E"Which regions have the highest call drop rates?"\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESemantic Model 2: Infrastructure Capacity\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Analyze bandwidth utilization and capacity planning\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Dimensions\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETOWER_ID, TOWER_NAME, REGION, EQUIPMENT_STATUS\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Facts\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ETOTAL_BANDWIDTH_GBPS, USED_BANDWIDTH_GBPS, UTILIZATION_PCT\u003C/li\u003E\u003Cli\u003EEXPECTED_GROWTH_PCT, UPGRADE_RECOMMENDED\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample Questions\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E"Which towers are operating at over 80% capacity?"\u003C/li\u003E\u003Cli\u003E"Which towers need infrastructure upgrades?"\u003C/li\u003E\u003Cli\u003E"Which towers will run out of capacity in the next 6 months?"\u003C/li\u003E\u003Cli\u003E"Show me 5G towers with upgrade recommended"\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003ESemantic Model 3: Customer Feedback\u003C/h3\u003E\n","\u003Cp\u003E\u003Cstrong\u003EPurpose\u003C/strong\u003E: Analyze customer sentiment, complaints, and churn\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Dimensions\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EREGION, FEEDBACK_TYPE, CUSTOMER_SEGMENT, PLAN_TYPE\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EKey Facts\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECOMPLAINT_COUNT, AVG_SENTIMENT_SCORE, NETWORK_ISSUE_COUNT\u003C/li\u003E\u003Cli\u003EMONTHLY_REVENUE, TENURE_MONTHS, IS_CHURNED\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EExample Questions\u003C/strong\u003E:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E"Which regions have the most customer complaints?"\u003C/li\u003E\u003Cli\u003E"What is the trend in customer sentiment over the past month?"\u003C/li\u003E\u003Cli\u003E"What are the top reasons for customer churn?"\u003C/li\u003E\u003Cli\u003E"Show me customers at risk of churning"\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EExplore Cortex Analyst in the UI\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003EFrom the navigation bar, click \u003Cstrong\u003EAI & ML\u003C/strong\u003E → \u003Cstrong\u003EStudio\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EThe semantic models are available via the stage \u003Ccode\u003E@TELCO_OPERATIONS_AI.CORTEX_ANALYST.CORTEX_ANALYST\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EKey Insights\u003C/h3\u003E\n","\u003Cp\u003E✅ \u003Cstrong\u003ESemantic Models\u003C/strong\u003E bridge business language and database schemas\n✅ \u003Cstrong\u003EVerified Queries\u003C/strong\u003E provide pre-tested SQL for common questions\n✅ \u003Cstrong\u003ENatural Language\u003C/strong\u003E enables non-technical users to query data\n✅ \u003Cstrong\u003EMultiple Models\u003C/strong\u003E allow domain-specific analysis\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENext\u003C/strong\u003E: These semantic models become tools for the Snowflake CoWork Agent!\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIntelligence Agent\u003C/h2\u003E\n","\u003Ch3\u003EOverview: The Telco Operations AI Agent\u003C/h3\u003E\n","\u003Cp\u003EThe \u003Cstrong\u003ETelco Operations AI Agent\u003C/strong\u003E is your AI-powered telecommunications operations assistant that combines multiple data sources, search capabilities, and analytical tools to provide comprehensive insights about network performance, customer experience, and operational efficiency.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EWhat the Agent Can Do\u003C/strong\u003E:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E📊 Analyze network performance metrics and identify issues\u003C/li\u003E\u003Cli\u003E🏗️ Monitor infrastructure capacity and recommend upgrades\u003C/li\u003E\u003Cli\u003E💬 Search customer call transcripts for specific topics\u003C/li\u003E\u003Cli\u003E📧 Search support tickets for issue patterns\u003C/li\u003E\u003Cli\u003E📈 Track customer sentiment and churn risk\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis agent represents the \u003Cstrong\u003Eculmination of all the previous work\u003C/strong\u003E - it brings together the unstructured data processing, search services, and semantic models into one conversational interface.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EAccess the Agent\u003C/h3\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/snowflake-intelligence-ui.jpg\" alt=\"Snowflake CoWork\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E → \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E in your Snowflake account\u003C/li\u003E\u003Cli\u003ESelect the \u003Cstrong\u003ETelco Operations AI Agent\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EThe agent will automatically select the right tools based on your question\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cstrong\u003ELocation\u003C/strong\u003E: \u003Ccode\u003ESNOWFLAKE_INTELLIGENCE.AGENTS."Telco Operations AI Agent"\u003C/code\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/intelligence-navigation.png\" alt=\"Intelligence Tab Navigation\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EAgent Architecture: 5 Powerful Tools\u003C/h3\u003E\n","\u003Cp\u003EThe Telco Operations AI Agent has access to \u003Cstrong\u003E5 different tools\u003C/strong\u003E that it automatically orchestrates based on your questions:\u003C/p\u003E\n","\u003Ch4\u003ECortex Analyst Tools (3)\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003E1. network_performance\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ENetwork performance metrics for cell towers across regions\u003C/li\u003E\u003Cli\u003ELatency, speed measurements, user activity, service quality\u003C/li\u003E\u003Cli\u003EUses: \u003Ccode\u003Enetwork_performance.yaml\u003C/code\u003E semantic model\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E2. Customer_Feedback\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ECustomer feedback analytics with sentiment analysis\u003C/li\u003E\u003Cli\u003EFeedback categorization and customer details including churn\u003C/li\u003E\u003Cli\u003EUses: \u003Ccode\u003Ecustomer_feedback.yaml\u003C/code\u003E semantic model\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E3. Infrastructure_Capacity\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInfrastructure capacity metrics for telecom towers\u003C/li\u003E\u003Cli\u003EBandwidth utilization, equipment status, capacity planning\u003C/li\u003E\u003Cli\u003EUses: \u003Ccode\u003Einfrastructure_capacity.yaml\u003C/code\u003E semantic model\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ECortex Search Tools (2)\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003E4. CALL_TRANSCRIPTS\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESearch customer call transcripts for specific issues or keywords\u003C/li\u003E\u003Cli\u003EIncludes sentiment scores and speaker identification\u003C/li\u003E\u003Cli\u003EService: \u003Ccode\u003ECALL_TRANSCRIPT_SEARCH\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003E5. SUPPORT_TICKETS\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESearch support tickets for issue tracking and resolution\u003C/li\u003E\u003Cli\u003EIncludes priority, category, and status information\u003C/li\u003E\u003Cli\u003EService: \u003Ccode\u003ESUPPORT_TICKET_SEARCH\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EWhat the Agent Can Do\u003C/h3\u003E\n","\u003Ch4\u003E1. Network Performance Analysis\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry asking:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Which regions have the highest network latency issues?"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EQuery the network_performance semantic model\u003C/li\u003E\u003Cli\u003EAnalyze latency metrics by region\u003C/li\u003E\u003Cli\u003ERank regions by average latency\u003C/li\u003E\u003Cli\u003EShow you towers that need attention\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBehind the scenes:\u003C/strong\u003E Uses the \u003Ccode\u003Enetwork_performance\u003C/code\u003E Cortex Analyst tool to generate and execute SQL.\u003C/p\u003E\n","\u003Ch4\u003E2. Capacity Planning\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry asking:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Show me 5G towers operating above 80% capacity"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EQuery infrastructure capacity data\u003C/li\u003E\u003Cli\u003EFilter for 5G towers with high utilization\u003C/li\u003E\u003Cli\u003EShow capacity exhaustion timelines\u003C/li\u003E\u003Cli\u003ERecommend upgrades where needed\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EData sources:\u003C/strong\u003E Infrastructure capacity metrics with utilization percentages and growth projections.\u003C/p\u003E\n","\u003Ch4\u003E3. Customer Call Analysis\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry asking:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Find calls mentioning network connectivity problems"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003ESearch call transcripts semantically\u003C/li\u003E\u003Cli\u003EFind conversations about connectivity issues\u003C/li\u003E\u003Cli\u003EShow sentiment scores for matching calls\u003C/li\u003E\u003Cli\u003EIdentify patterns in customer complaints\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EDemonstrates:\u003C/strong\u003E Semantic search across transcribed call recordings.\u003C/p\u003E\n","\u003Ch4\u003E4. Customer Churn Analysis\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry asking:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Which customer segments have the highest churn risk?"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAnalyze customer feedback and churn data\u003C/li\u003E\u003Cli\u003ESegment by customer type and plan\u003C/li\u003E\u003Cli\u003EIdentify at-risk customers\u003C/li\u003E\u003Cli\u003ECorrelate with complaint patterns\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E5. Cross-Domain Insights\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry asking:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Are network issues correlated with negative customer sentiment?"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EQuery both network performance and customer feedback\u003C/li\u003E\u003Cli\u003ECorrelate latency spikes with complaint volumes\u003C/li\u003E\u003Cli\u003EIdentify regions with both technical and satisfaction issues\u003C/li\u003E\u003Cli\u003EProvide actionable insights\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003E6. Cross-Domain Analysis\u003C/h4\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETry asking:\u003C/strong\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E"Are network issues correlated with negative customer sentiment?"\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EThe agent will:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EQuery both network performance and customer feedback\u003C/li\u003E\u003Cli\u003ECorrelate latency spikes with complaint volumes\u003C/li\u003E\u003Cli\u003EIdentify regions with both technical and satisfaction issues\u003C/li\u003E\u003Cli\u003EProvide actionable insights\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESample Conversation Flow\u003C/h3\u003E\n","\u003Cp\u003EHere's an example conversation demonstrating the agent's capabilities:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYou:\u003C/strong\u003E "Which regions have the highest network latency issues?"\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAgent:\u003C/strong\u003E \u003Cem\u003EQueries network performance, shows regions ranked by average latency\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYou:\u003C/strong\u003E "Show me 5G towers operating above 80% capacity"\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAgent:\u003C/strong\u003E \u003Cem\u003EQueries infrastructure capacity, lists overloaded towers with utilization %\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYou:\u003C/strong\u003E "Find calls mentioning network connectivity problems"\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAgent:\u003C/strong\u003E \u003Cem\u003ESearches call transcripts, returns matching conversations with sentiment\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EYou:\u003C/strong\u003E "What are the top 3 customer complaints in October 2025?"\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAgent:\u003C/strong\u003E \u003Cem\u003EQueries customer feedback, shows complaint categories ranked\u003C/em\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ESample Questions to Try\u003C/h3\u003E\n","\u003Cp\u003EHere are the sample questions configured in the agent:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/intelligence-sample-questions.jpg\" alt=\"Agent Sample Questions\"\u003E\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E"Which regions have the highest network latency issues?"\u003C/li\u003E\u003Cli\u003E"Show me 5G towers operating above 80% capacity"\u003C/li\u003E\u003Cli\u003E"Find calls mentioning network connectivity problems"\u003C/li\u003E\u003Cli\u003E"What are the top 3 customer complaints in October 2025?"\u003C/li\u003E\u003Cli\u003E"Find support tickets about billing issues"\u003C/li\u003E\u003Cli\u003E"Which customer segments have the highest churn risk?"\u003C/li\u003E\u003Cli\u003E"Are network issues correlated with negative customer sentiment?"\u003C/li\u003E\u003Cli\u003E"Show me regions with both high latency and customer complaints"\u003C/li\u003E\u003Cli\u003E"What percentage of calls mention competitor names?"\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-query-response-1.png\" alt=\"Agent Testing Example\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-query-response-2.png\" alt=\"Agent Testing Example 2\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EEditing and Understanding the Agent\u003C/h3\u003E\n","\u003Cp\u003EAfter using the agent, explore its configuration:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAI & ML Studio\u003C/strong\u003E → \u003Cstrong\u003EAgents\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick on \u003Cstrong\u003ETelco Operations AI Agent\u003C/strong\u003E\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EEdit\u003C/strong\u003E to view:\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESample Questions\u003C/strong\u003E: The questions you just tried\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETools\u003C/strong\u003E: All 5 tools available to the agent\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EOrchestration Instructions\u003C/strong\u003E: How the agent decides which tools to use\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAccess Control\u003C/strong\u003E: Who can use the agent\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/create-agent-button.png\" alt=\"Create Agent Button\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EAgent Configuration Details\u003C/h3\u003E\n","\u003Cp\u003EWhen configuring the agent, you'll see these key sections:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAgent Description:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-description-form.png\" alt=\"Describe Agent\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESemantic Model YAML Files:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThe agent uses 3 semantic models located in the CORTEX_ANALYST stage:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/semantic-model-yaml-files.jpg\" alt=\"YAML Files\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAdd Tools with Detailed Descriptions:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/tool-description-generate.png\" alt=\"Detailed Description\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EAll 3 Semantic Model Tools:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/semantic-model-tools-added.png\" alt=\"All 3 Semantic Model Tools\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECortex Search Service Configuration:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/cortex-search-transcripts-config.jpg\" alt=\"Call Transcripts Search\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/cortex-search-services-list.jpg\" alt=\"Search Services\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ETest Your Agent:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EThe Test Agent interface is where you can interact with your AI assistant in real-time before publishing it. Here you can:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EAsk natural language questions\u003C/strong\u003E about network performance, customer feedback, and infrastructure capacity\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EVerify tool selection\u003C/strong\u003E - watch which tools (Cortex Analyst semantic models or Cortex Search services) the agent chooses to answer each question\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EReview SQL generation\u003C/strong\u003E - see the actual SQL queries generated by Cortex Analyst for structured data questions\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETest multi-tool queries\u003C/strong\u003E - try complex questions that require the agent to combine data from multiple sources\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERefine responses\u003C/strong\u003E - iterate on your tool descriptions and instructions to improve answer quality\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDebug issues\u003C/strong\u003E - identify when the agent selects the wrong tool or misunderstands a question\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETry questions like: \u003Cem\u003E"What regions have the highest call drop rates?"\u003C/em\u003E or \u003Cem\u003E"What are customers saying about 5G coverage?"\u003C/em\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-test-interface.png\" alt=\"Test Agent\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EConfigure Sample Questions:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-sample-questions.jpg\" alt=\"Agent Questions\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EData Coverage\u003C/h3\u003E\n","\u003Ch4\u003ENetwork Data\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMultiple Regions\u003C/strong\u003E: Geographic coverage across all operational areas\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ENetwork Types\u003C/strong\u003E: 4G and 5G towers\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPerformance Metrics\u003C/strong\u003E: Latency, speed, packet loss, availability\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003ECustomer Data\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ECustomer Segments\u003C/strong\u003E: Consumer, Business, Enterprise\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPlan Types\u003C/strong\u003E: Premium 5G, Standard 4G, Business Plan\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EChurn Analysis\u003C/strong\u003E: Reasons, at-risk identification\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EInteraction Data\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E25 Call Recordings\u003C/strong\u003E: Transcribed with AI_TRANSCRIBE\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESupport Tickets\u003C/strong\u003E: Categorized by type and priority\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EFeedback\u003C/strong\u003E: Sentiment analyzed by channel and region\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EConfigure Snowflake CoWork Settings\u003C/h3\u003E\n","\u003Cp\u003ETo customize the appearance of your Intelligence interface:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to \u003Cstrong\u003EAI & ML\u003C/strong\u003E → \u003Cstrong\u003EAgents\u003C/strong\u003E → \u003Cstrong\u003ESnowflake CoWork\u003C/strong\u003E tab\u003C/li\u003E\u003Cli\u003EClick \u003Cstrong\u003EOpen Settings\u003C/strong\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/intelligence-settings.png\" alt=\"Intelligence Settings Panel\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECustomize the Interface:\u003C/strong\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDisplay Name\u003C/strong\u003E: "NovaConnect Intelligence" or your organization name\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDescription\u003C/strong\u003E: Brief description of what the agent can help with\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EWelcome Message\u003C/strong\u003E: Greeting users see when starting a conversation\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EColor Theme\u003C/strong\u003E: Match your brand colors\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBrand Your Intelligence with Logos:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EUse the NovaConnect logos to brand your interface:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/novaconnect-logo.png\" alt=\"NovaConnect Full Logo\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/novaconnect-logo-icon.png\" alt=\"NovaConnect Compact Logo\"\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003ETips for Best Results\u003C/h3\u003E\n","\u003Ch4\u003EAsk Specific Questions\u003C/h4\u003E\n","\u003Cp\u003E✅ \u003Cstrong\u003EGood\u003C/strong\u003E: "Which 5G towers have latency above 25ms?"\n❌ \u003Cstrong\u003EToo vague\u003C/strong\u003E: "Tell me about the network"\u003C/p\u003E\n","\u003Ch4\u003EUse Follow-up Questions\u003C/h4\u003E\n","\u003Cp\u003EThe agent maintains context, so you can:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAsk initial question\u003C/li\u003E\u003Cli\u003EDrill deeper on specific findings\u003C/li\u003E\u003Cli\u003ERequest different visualizations\u003C/li\u003E\u003Cli\u003EAsk for comparisons\u003C/li\u003E\u003C/ol\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EUnderstanding Agent Responses\u003C/h3\u003E\n","\u003Ch4\u003EWhen You See Charts\u003C/h4\u003E\n","\u003Cp\u003EThe agent automatically generates visualizations based on the orchestration instruction to convert to logs when comparing measures.\u003C/p\u003E\n","\u003Ch4\u003EWhen You See "Checking multiple sources..."\u003C/h4\u003E\n","\u003Cp\u003EThe agent is:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EQuerying semantic models\u003C/li\u003E\u003Cli\u003ESearching search services\u003C/li\u003E\u003Cli\u003ECombining results from multiple tools\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThis ensures comprehensive, validated answers.\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch3\u003EData Limitations & Disclaimers\u003C/h3\u003E\n","\u003Ch4\u003ESynthetic Data\u003C/h4\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ENovaConnect\u003C/strong\u003E: Entirely fictional telecommunications company\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EAll metrics\u003C/strong\u003E: Synthetic but industry-realistic\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ECustomer data\u003C/strong\u003E: Generated for demonstration purposes\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch4\u003EPurpose\u003C/h4\u003E\n","\u003Cp\u003EThis is a \u003Cstrong\u003Edemonstration environment\u003C/strong\u003E showing telecommunications AI capabilities. \u003Cstrong\u003EDo not make actual business decisions based on this data!\u003C/strong\u003E\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ECustomize Regions (Optional)\u003C/h2\u003E\n","\u003Cp\u003EDuration: 5\u003C/p\u003E\n","\u003Ch3\u003EMake the Demo Your Own\u003C/h3\u003E\n","\u003Cp\u003EThe sample data uses Malaysian regions by default (Kuala Lumpur, Selangor, Penang, etc.). If you'd like to customize the demo to use regions relevant to your geography, we've included a customization script.\u003C/p\u003E\n","\u003Ch3\u003ERunning the Customization Script\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003ENavigate to the SQL script in your Git repository:\u003C/li\u003E\u003C/ol\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Open from Snowflake UI or run directly\n@TELCO_AI_REPO/branches/main/assets/sql/99_customize_regions.sql\n\u003C/code\u003E\u003C/pre\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EOr create a new SQL worksheet and copy the script from:\n\u003Ccode\u003Eassets/sql/99_customize_regions.sql\u003C/code\u003E\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EAvailable Presets\u003C/h3\u003E\n","\u003Cp\u003EThe script includes several preset configurations you can uncomment:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPreset\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EExample Regions\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EUS Cities\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENew York Metro, Los Angeles, Chicago, Houston, Phoenix\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EEuropean\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ELondon, Paris, Berlin, Madrid, Rome, Amsterdam\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Cstrong\u003EGeneric\u003C/strong\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERegion Alpha, Region Beta, Region Gamma, etc.\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003ECustom Mapping\u003C/h3\u003E\n","\u003Cp\u003ETo create your own mapping, modify the \u003Ccode\u003EINSERT INTO REGION_MAPPING\u003C/code\u003E statements:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EINSERT INTO REGION_MAPPING VALUES\n    -- (Original Region, Your New Region, Original Prefix, Your New Prefix)\n    ('Kuala Lumpur', 'Your City Name', 'KL', 'YCN'),\n    ('Selangor', 'Another Region', 'SEL', 'AR'),\n    -- ... add more as needed\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EWhat Gets Updated\u003C/h3\u003E\n","\u003Cp\u003EThe script updates the following tables:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ENETWORK_PERFORMANCE\u003C/code\u003E - Tower regions and names\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EINFRASTRUCTURE_CAPACITY\u003C/code\u003E - Tower regions and names\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003ECUSTOMER_FEEDBACK_SUMMARY\u003C/code\u003E - Feedback regions\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAfter running the customization, your agent queries will return results using your custom region names!\u003C/p\u003E\n\u003Chr\u003E\n","\u003Ch2\u003EConclusion\u003C/h2\u003E\n","\u003Ch3\u003EWhat You've Accomplished\u003C/h3\u003E\n","\u003Cp\u003EIn this quickstart, you've built a comprehensive AI-powered telecommunications operations platform using Snowflake's Cortex AI capabilities. You started by processing unstructured data—extracting insights from PDF help documents using Cortex Document Processing, transcribing customer call recordings with AI_TRANSCRIBE, and analyzing sentiment across thousands of data points. This transformed raw, unstructured telecommunications content into structured, queryable data that forms the foundation of intelligent applications.\u003C/p\u003E\n","\u003Cp\u003EYou then created the AI services layer that makes this data accessible and actionable. Cortex Search services enable semantic search across call transcripts and support tickets—finding relevant information based on meaning rather than keywords. Cortex Analyst semantic models allow natural language queries against structured network and customer data, automatically generating SQL from plain English questions. Together, these services power the intelligence layer that connects users to insights without requiring technical expertise.\u003C/p\u003E\n","\u003Cp\u003EFinally, you deployed and explored the Snowflake CoWork Agent—the "Telco Operations AI Agent" that orchestrates all of these capabilities through a conversational interface. The agent seamlessly combines search services and semantic models to answer complex operational questions and generate visualizations. This demonstrates how modern AI platforms can unify structured and unstructured data analysis, enabling faster decision-making and deeper insights across telecommunications operations use cases.\u003C/p\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ERepository\u003C/strong\u003E: \u003Ca href=\"https://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence\"\u003EGitHub - Telco AI Assistant\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDocumentation\u003C/strong\u003E: See README.md in repository\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Cp\u003E\u003Cstrong\u003EReady to build AI-powered applications with Snowflake Cortex?\u003C/strong\u003E Start experimenting with your own data today!\u003C/p\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/guides-overview-ai-features\"\u003ESnowflake AI and ML Features\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence\"\u003ESnowflake CoWork Overview\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/solutions/industries/telecommunications/\"\u003ESnowflake for Telecommunications\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Chr\u003E\n","\u003Ch2\u003ERe-deploying / Reset\u003C/h2\u003E\n","\u003Cp\u003ESince the Git repo is in a separate database, you can easily reset:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Drop the main database (Git repo stays safe!)\nDROP DATABASE IF EXISTS TELCO_OPERATIONS_AI;\nDROP DATABASE IF EXISTS SNOWFLAKE_INTELLIGENCE;\n\n-- Fetch latest code\nALTER GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO FETCH;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThen re-run deployment scripts (01-05) from the Git Repositories UI as described in Step 3.\u003C/p\u003E"],"title":"Build an AI Assistant for Telco using AI SQL and Snowflake CoWork","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\n\n## Overview\n\n### Introduction\n\nTelecommunications companies face unique challenges in managing vast amounts of operational data—from network performance metrics and infrastructure capacity to customer call recordings, support tickets, and satisfaction surveys. To address these challenges effectively, a unified platform is essential—one capable of storing and processing all data types, whether MP3 call recordings, PDF help documents, or structured network telemetry.\n\nThroughout this quickstart, we'll use **NovaConnect**—a fictitious telecommunications company—as our example organization. NovaConnect operates a nationwide 5G network serving millions of customers, and needs AI-powered tools to analyze network performance, understand customer feedback, and streamline support operations.\n\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/novaconnect-logo.png\" alt=\"NovaConnect Logo\" width=\"75%\"/\u003E\n\nThis data must be readily accessible for analysis using the latest large language models (LLMs) such as Anthropic, Gemma, LLaMA, or DeepSeek. Ensuring the trustworthiness and security of generated insights is critical, especially when they inform network operations decisions and customer experience strategies.\n\nIn addition, developing Agentic AI capabilities allows for natural language question-answering tailored to network engineers, customer service managers, and operations teams who need real-time insights into network health and customer satisfaction.\n\n**In this hands-on lab, you'll learn how to build a Telco Operations AI Agent from the ground up using the Snowflake AI Data Cloud.**\n\nLearn how you can leverage the latest AI technologies right within the Snowflake platform. When AI is deeply embedded in your trusted data platform, the possibilities are endless. We will be exploring the processing of both **Unstructured** and **Structured** data which will then allow the application of a **Cortex Agent** to help discover insights by leveraging **All Data.**\n\n### Data Sources and Analytics\n\nThis hands-on lab utilizes a comprehensive telecommunications dataset spanning multiple data types to demonstrate real-world AI applications. You'll work with:\n\n**Network Operations Data**\n\n- **Network Performance Metrics** - Latency, download/upload speeds, packet loss, and availability from cell towers\n- **Infrastructure Capacity** - Bandwidth utilization, equipment status, and capacity planning data\n- **4G/5G Tower Analytics** - Regional performance metrics across multiple geographic regions\n\n**Customer Experience Data**\n\n- **25 Call Recordings** (MP3) - Customer support calls transcribed with AI_TRANSCRIBE\n- **8 Help Documents** (PDF) - Product guides and troubleshooting documents processed with AI_PARSE_DOCUMENT\n- **Customer Feedback Summary** - Sentiment analysis, complaints, and feedback categorization\n- **Customer Details** - Demographics, plan types, tenure, and churn analysis\n- **Support Tickets** - Issue tracking with priority, category, and resolution data\n- **CSAT Surveys** - Customer satisfaction scores and NPS ratings\n\n**AI-Powered Analysis Journey**\n\nYour AI assistant will synthesize insights from all these sources to answer questions like:\n\n- *\"Which regions have the highest network latency issues?\"*\n- *\"Show me 5G towers operating above 80% capacity\"*\n- *\"Find calls mentioning network connectivity problems\"*\n- *\"Which customer segments have the highest churn risk?\"*\n- *\"What are the top customer complaints this month?\"*\n\nThis diverse dataset enables you to experience how modern AI can unify structured network data with unstructured customer interactions, creating comprehensive operational intelligence.\n\n\u003E **⚠️ Important Disclaimer:** All data including NovaConnect company information is completely synthetic and created for educational purposes. Business decisions cannot be made based on any outcomes of this lab.\n\n### What You'll Build\n\nIn this quickstart, you'll build a comprehensive AI-powered telecommunications operations platform called **NovaConnect Intelligence** using Snowflake's Cortex AI capabilities. This end-to-end solution demonstrates how to:\n\n- Process unstructured documents (PDFs) with **Cortex Document Processing**\n- Transcribe and analyze customer call audio with **AI Transcribe** and **AI Sentiment**\n- Create intelligent search experiences with **Cortex Search Services**\n- Build natural language data queries with **Cortex Analyst** and semantic models\n- Deploy conversational AI agents with **Snowflake CoWork**\n\n### What You'll Learn\n\n- How to extract structured data from unstructured documents\n- How to build and configure Cortex Search Services for RAG applications\n- How to create Cortex Analyst semantic models for business intelligence\n- How to use Snowflake CoWork agents with multiple tools\n- How to deploy production-ready Streamlit applications in Snowflake\n\n### What You'll Need\n\n- A Snowflake account (free trial works for most features) with **ACCOUNTADMIN** access\n- Web browser (Chrome, Firefox, or Safari)\n- Basic knowledge of SQL\n- 15-20 minutes for deployment\n\n### Deployment Options\n\nThis quickstart offers two deployment paths to accommodate different preferences and skill levels. Choose the option that best fits your workflow:\n\n#### Option 1: Snowsight UI (Recommended)\n\n**Best for:** Most users, visual learners, those new to Snowflake\n\nDeploy directly from GitHub using Snowflake's built-in Git integration. This approach:\n- **No local setup required** - Everything runs in your browser\n- **Visual feedback** - Watch each step execute in Snowsight\n- **Easy troubleshooting** - See errors immediately in the UI\n- **Learn as you go** - Understand what each script does\n\nYou'll connect Snowflake to GitHub, then run a series of SQL scripts that automatically:\n1. Configure your account and create required objects\n2. Load sample data from stages\n3. Deploy Cortex Search services and Analyst semantic models\n4. Create notebooks for data processing\n\n#### Option 2: Snowflake CoCo CLI (Alternative)\n\n**Best for:** Developers, automation enthusiasts, CLI power users\n\nUse the Telco Agent Builder skill for guided, conversational deployment. This approach:\n- **Conversational interface** - AI guides you through deployment\n- **Automated execution** - Less manual copying/pasting\n- **Skill-based** - Uses Snowflake CoCo's agent capabilities\n\n### Assets Created\n\nBy the end of this lab, you'll have deployed:\n\n- **Database**: `TELCO_OPERATIONS_AI` with multiple schemas\n- **Warehouse**: `TELCO_WH` (Medium)\n- **Role**: `TELCO_ANALYST_ROLE` with CORTEX_USER privileges\n- **14+ Tables** with ~10,000 rows of telco data\n- **2 Cortex Search Services** (call transcripts, support tickets)\n- **3 Cortex Analyst Semantic Models** (network, infrastructure, customer)\n- **1 Snowflake CoWork Agent** (Telco Operations AI Agent)\n- **3 Snowflake Notebooks** for data processing\n- **33+ Files** (25 MP3 audio, 8 PDF documents)\n\n\u003C!-- ------------------------ --\u003E\n\n## Architecture Overview\n\n### Multi-Modal AI Platform\n\nThis quickstart deploys a **complete multi-modal AI platform** combining:\n\n**Data Sources** → **AI Processing** → **Structured Data** → **AI Services** → **Applications**\n\n**Data Types**:\n\n- 📄 Documents (PDFs - help guides, product information)\n- 🎙️ Audio (MP3 customer support calls)\n- 📊 Structured (Network metrics, customer data, 10,000+ rows)\n- 📧 Support Tickets (Customer issues and resolutions)\n- 📱 Customer Feedback (Sentiment analysis, complaints)\n\n**AI Capabilities**:\n\n- Cortex Document Processing (AI_PARSE_DOCUMENT, AI_EXTRACT)\n- Audio AI (AI_TRANSCRIBE with timestamps)\n- Sentiment AI (AI_SENTIMENT for call analysis)\n- Translation AI (AI_TRANSLATE for multi-language support)\n- Aggregation AI (AI_AGG without context limits)\n\n**Latest AISQL Syntax**: All examples use 2025 AI_* functions\n\n### System Architecture\n\nThe NovaConnect AI platform follows a layered architecture that transforms raw unstructured data into actionable intelligence. Data flows from multiple sources (documents, audio, structured tables) through Cortex AI processing functions, into organized tables and search indices, and finally surfaces through Snowflake CoWork agents and applications.\n\nThe architecture diagram below illustrates how each component connects:\n\n- **Left side**: Raw data sources (PDFs, MP3s, CSVs) stored in Snowflake stages\n- **Middle**: AI processing layer using Cortex functions to extract, transcribe, and analyze content\n- **Right side**: Structured outputs (tables, search services, semantic models) that power the agent\n\n![Telco Operations AI Architecture](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/architecture-overview.jpg)\n\nThe dataflow diagram below shows how data moves through the system to power Snowflake CoWork. Raw data enters from the left (CSV files, PDF documents, MP3 audio recordings), gets processed through Cortex AI functions in the middle (AI_PARSE_DOCUMENT, AI_TRANSCRIBE, AI_SENTIMENT), and flows into the structured outputs on the right (Cortex Search services and Cortex Analyst semantic models). These services become the \"tools\" that the Snowflake CoWork agent uses to answer user questions—combining structured SQL queries with semantic search across unstructured content.\n\n![Architecture Diagram](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/architecture-diagram.png)\n\n### Key Technologies\n\n- **Cortex AI Functions**: AI_PARSE_DOCUMENT, AI_TRANSCRIBE, AI_SENTIMENT, AI_COMPLETE\n- **Cortex Search**: 2 search services for semantic search and RAG\n- **Cortex Analyst**: 3 semantic models for natural language SQL\n- **Snowflake CoWork**: Conversational agents with tool orchestration\n- **Cortex Document Processing**: Automated document processing at scale\n\n\u003C!-- ------------------------ --\u003E\n\n## Setup Your Environment\n\n### Step 1: Get a Snowflake Account\n\n**Option A - Free Trial** (Recommended):\n\n1. Visit https://signup.snowflake.com/\n2. Sign up for a free 30-day trial\n3. Choose **Enterprise** edition\n4. Select a cloud region (AWS, Azure, or GCP)\n5. Verify your email\n6. Log in to Snowsight (https://app.snowflake.com)\n\n**Option B - Existing Account**:\n\n- Use any Snowflake account with ACCOUNTADMIN access\n- Log in to Snowsight\n- No special setup required\n\n### Step 2: Connect to GitHub Repository in Snowflake\n\n**Deploy directly from GitHub - No downloads or CLI tools needed!**\n\n#### Step 2a: Create Git Integration (One-Time Setup)\n\n1. In **Snowsight**, click on **Projects**\n2. Select **Workspaces**\n3. Add new **SQL file**\n4. Copy and paste this script:\n\n```sql\n-- Setup Git Integration (one-time)\n-- This creates a SEPARATE database for Git repos so you can drop/recreate \n-- TELCO_OPERATIONS_AI without losing the Git integration\n\nUSE ROLE ACCOUNTADMIN;\n\n-- Create SEPARATE database for Git repositories (won't be dropped with main database)\nCREATE DATABASE IF NOT EXISTS TELCO_AI_LAB\n    COMMENT = 'Persistent database for Git repository integrations - DO NOT DROP';\nCREATE SCHEMA IF NOT EXISTS TELCO_AI_LAB.GIT_REPOS;\n\nUSE DATABASE TELCO_AI_LAB;\nUSE SCHEMA GIT_REPOS;\n\n-- Create API integration for GitHub\nCREATE OR REPLACE API INTEGRATION git_api_integration\n    API_PROVIDER = git_https_api\n    API_ALLOWED_PREFIXES = ('https://github.com/Snowflake-Labs/')\n    ENABLED = TRUE;\n\n-- Grant usage on API integration\nGRANT USAGE ON INTEGRATION git_api_integration TO ROLE ACCOUNTADMIN;\n\n-- Create Git repository object\nCREATE OR REPLACE GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO\n    API_INTEGRATION = git_api_integration\n    ORIGIN = 'https://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence.git';\n\n-- Grant READ permission on Git repository\nGRANT READ ON GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO TO ROLE ACCOUNTADMIN;\n\n-- Fetch code from GitHub\nALTER GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO FETCH;\n\nSELECT 'Git integration ready!' AS status,\n       'Git repo is in TELCO_AI_LAB database (separate from main database)' AS note;\n```\n\n4. Click **Run** (or press Cmd/Ctrl + Enter)\n5. Wait for completion (~30 seconds)\n6. ✅ **Git integration complete!** You're now connected to GitHub\n\n#### Step 2b: Access Git Repository in Snowflake UI\n\nNow will create a workspace from the github repository\n\n1. Create a Workspace from Github repository by clicking on My Workspace, then select the option **From Git repository**\n\n2. When Prompted, use the following URL in the **Repository URL** field:\n\n```text    \nhttps://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence.git\n```\n\n3. Press **Create**\n\n4. **Navigate to the assets folder** (at the root of the repository):\n\n**You should see the following file structure:**\n\n```\nassets/\n├── sql/                    ← Deployment scripts (START HERE)\n├── data/                   ← CSV files and PDFs\n├── audio/                  ← MP3 call recordings\n├── Notebooks/              ← Snowflake notebooks\n└── semantic_models/        ← YAML definitions\n```\n\n5. **Navigate to `sql/`** - This is where the deployment scripts are\n6. You'll see SQL files numbered 01-05\n\n✅ **You're now ready to deploy!**\n\n---\n\n### Step 3: Deploy from GitHub Using Git Integration\n\n#### Understanding GitHub Integration in Snowflake\n\nSnowflake's **Git Integration** feature allows you to connect directly to GitHub repositories and execute SQL scripts without downloading files locally. This is powerful because:\n\n- **Version Control**: Scripts are always up-to-date from the source repository\n- **No Downloads**: Execute directly from GitHub - no local files needed\n- **Reproducible**: Same scripts, same results every time\n- **Workspace Integration**: Browse, view, and edit files directly in Snowsight\n\nWhen you created the Git Repository object in Step 2, Snowflake established a connection to the Telco AI GitHub repository. This connection allows you to:\n\n1. **Browse files** in the repository through the Snowsight UI\n2. **Create Workspaces** from repository folders for interactive development\n3. **Fetch updates** when the repository changes\n\nThe deployment scripts in `assets/sql/` are numbered 01-05 and should be executed in order. Each script builds on the previous one, creating the complete NovaConnect AI platform.\n\n#### Deploy Using the Git Repositories UI\n\n1. Navigate: **Projects** → **Git Repositories** → **TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO**\n2. Browse to: `assets/sql/`\n3. Right-click each file (01-05) → \"Open in new worksheet\"\n4. Execute each script in order\n\n**What gets deployed**:\n\n1. ✅ Database `TELCO_OPERATIONS_AI` with schemas\n2. ✅ Role `TELCO_ANALYST_ROLE` with CORTEX_USER privileges\n3. ✅ 14+ tables with ~10,000 rows of data\n4. ✅ 2 Cortex Search Services\n5. ✅ 3 Cortex Analyst Semantic Models\n6. ✅ 1 Snowflake CoWork Agent\n7. ✅ 3 Notebooks\n9. ✅ Stages with MP3 audio files and PDFs\n\n**Deployment time**: 15-20 minutes\n\n---\n\n### Alternative: Deploy with Snowflake CoCo CLI\n\n**New!** You can also deploy this quickstart using the **Snowflake CoCo CLI** - Snowflake's AI-powered command-line assistant.\n\n#### Option C: Automated Deployment with Snowflake CoCo\n\nIf you have Snowflake CoCo CLI installed, you can use the built-in **skills** for guided, automated deployment. Skills are structured markdown instructions that guide Snowflake CoCo through complex deployment procedures.\n\n**1. Clone the Repository**\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence.git\ncd sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence\n```\n\n**2. Configure Your Snowflake Connection** (if not already configured)\n\n```bash\n# List available connections\nsnow connection list\n\n# Or create a new connection\nsnow connection add\n```\n\n**3. Register the Project Skills**\n\nThe repository includes custom skills in `.cortex/skills/`, but they need to be registered before use. Run this command once to add the skills directory:\n\n```bash\n# Register the skills directory (one-time setup)\ncortex skill add .cortex/skills\n\n# Verify the skills were added\ncortex skill list\n```\n\nYou should see output like:\n```\nAdded skill directory: /path/to/.cortex/skills\nSkills found: telco-agent-builder, telco-agent-uninstall\n```\n\n\u003E **Note**: Skills require a file named `SKILL.md` (uppercase) in each skill subdirectory. The repository is pre-configured with this structure.\n\n**4. Launch Snowflake CoCo**\n\nStart Snowflake CoCo from the project directory:\n\n```bash\ncortex\n```\n\n**5. Deploy Using the Skill**\n\nOnce Snowflake CoCo is running, simply ask it to deploy. Use any of these prompts:\n\n```\n\u003E Deploy the NovaConnect Telco Operations AI quickstart\n\u003E Build the telco agent\n\u003E Set up the telecommunications agent\n```\n\nSnowflake CoCo will load the `telco-agent-builder` skill and guide you through each step interactively.\n\n**6. (Optional) Customize Regions Before Deployment**\n\nThe skill will ask if you want to customize the demo regions. By default, the data uses Malaysian regions (Kuala Lumpur, Selangor, Penang, etc.). \n\nIf you prefer different regions, the skill will run a Python script to generate customized CSV files:\n\n```bash\n# US Cities preset\npython assets/scripts/generate_regional_data.py --preset us\n\n# European Cities preset\npython assets/scripts/generate_regional_data.py --preset european\n\n# Generic regions (Region Alpha, Region Beta, etc.)\npython assets/scripts/generate_regional_data.py --preset generic\n\n# Custom mapping\npython assets/scripts/generate_regional_data.py --mapping \"Kuala Lumpur:Sydney,Selangor:Melbourne\"\n```\n\nThis generates new CSV files in `assets/data/regional/` which the skill will upload instead of the defaults.\n\n---\n\n#### Available Skills\n\nThe repository includes two skills in `.cortex/skills/`:\n\n| Skill | Description | Trigger Phrases |\n|-------|-------------|-----------------|\n| **telco-agent-builder** | Deploy the complete solution | \"deploy telco\", \"build the agent\", \"set up novaconnect\" |\n| **telco-agent-uninstall** | Clean up all resources | \"uninstall telco\", \"remove the agent\", \"cleanup\" |\n\n---\n\n#### What the Deployment Skill Does\n\nThe **telco-agent-builder** skill guides you through these steps:\n\n1. ✅ **Verify prerequisites** - Check Snowflake connection and permissions\n2. ✅ **Customize regions** (optional) - Generate regional data if requested\n3. ✅ **Configure account** - Create role, warehouse, database, schemas, and stages\n4. ✅ **Upload data** - Copy CSV, PDF, and MP3 files to Snowflake stages\n5. ✅ **Load tables** - Create and populate 14+ tables with telco data\n6. ✅ **Deploy Cortex Search** - Create 2 semantic search services\n7. ✅ **Deploy Cortex Analyst** - Upload semantic models and create the Intelligence Agent\n8. ✅ **Deploy Notebooks** - Create 3 Snowflake Notebooks\n9. ✅ **Verify deployment** - Run checks to confirm everything is working\n\n---\n\n#### Uninstalling with Snowflake CoCo\n\nTo remove all deployed resources, use the uninstall skill:\n\n```\n\u003E Uninstall the telco agent\n\u003E Clean up the NovaConnect deployment\n\u003E Remove the telco quickstart\n```\n\nThis will drop the database, warehouse, role, and all related objects.\n\n---\n\n#### Benefits of CLI Deployment\n\n- **Interactive guidance** - Step-by-step with explanations at each stage\n- **Error handling** - Automatic troubleshooting and suggested fixes\n- **Progress tracking** - Real-time status updates\n- **Customization** - Easily modify regions, settings, or skip optional steps\n- **Idempotent** - Safe to re-run if interrupted\n\n---\n\n#### Troubleshooting Skills\n\n**Skills not found?** If Snowflake CoCo doesn't recognize the skills, verify they're registered:\n\n```bash\n# Check registered skills\ncortex skill list\n\n# If not listed, add the skills directory\ncortex skill add .cortex/skills\n```\n\n**\"No valid skills found\" error?** Skills require a specific file structure:\n- Each skill must be in its own subdirectory (e.g., `.cortex/skills/telco-agent-builder/`)\n- The skill file must be named `SKILL.md` (uppercase, not `skill.md` or `\u003Cname\u003E.md`)\n\n**To remove a registered skill directory:**\n```bash\ncortex skill remove .cortex/skills\n```\n\n\u003E **Note**: Snowflake CoCo CLI is currently in Private Preview. Contact your Snowflake account team to request access.\n\n---\n\n### Step 4: Verify Deployment\n\nAfter deployment completes, verify in Snowflake UI:\n\n```sql\n-- Check all components\nUSE DATABASE TELCO_OPERATIONS_AI;\n\nSHOW TABLES IN SCHEMA DEFAULT_SCHEMA;              -- Should see 14+ tables\nSHOW CORTEX SEARCH SERVICES IN SCHEMA DEFAULT_SCHEMA; -- Should see 2 services\nSHOW NOTEBOOKS IN SCHEMA NOTEBOOKS;                -- Should see 3 notebooks\nSHOW AGENTS IN SCHEMA SNOWFLAKE_INTELLIGENCE.AGENTS;  -- Should see Telco Operations AI Agent\n```\n\n**All set?** ✅ Continue to the next section!\n\n\u003C!-- ------------------------ --\u003E\n\n## Verify Your Deployment\n\nAfter Git integration deployment completes, let's verify everything was created successfully.\n\n### Check Database Objects\n\nOpen Snowflake UI and navigate to **Data** → **Databases** → **TELCO_OPERATIONS_AI**\n\nYou should see:\n\n- ✅ **DEFAULT_SCHEMA** - Main data tables\n- ✅ **CORTEX_ANALYST** - Semantic model stage and views\n- ✅ **NOTEBOOKS** - Snowflake notebooks\n- ✅ **STREAMLIT** - Streamlit applications schema\n- ✅ **MODELS** - ML models and UDFs schema\n\n### Verify Tables (14+)\n\n```sql\nUSE DATABASE TELCO_OPERATIONS_AI;\nUSE SCHEMA DEFAULT_SCHEMA;\n\n-- Show all tables\nSHOW TABLES;\n\n-- Verify key table counts\nSELECT 'network_performance' AS table_name, COUNT(*) AS rows FROM network_performance\nUNION ALL SELECT 'infrastructure_capacity', COUNT(*) FROM infrastructure_capacity\nUNION ALL SELECT 'customer_details', COUNT(*) FROM customer_details\nUNION ALL SELECT 'customer_feedback_summary', COUNT(*) FROM customer_feedback_summary\nUNION ALL SELECT 'CALL_TRANSCRIPTS', COUNT(*) FROM CALL_TRANSCRIPTS\nUNION ALL SELECT 'AGENT_PERFORMANCE', COUNT(*) FROM AGENT_PERFORMANCE\nUNION ALL SELECT 'NETWORK_INCIDENTS', COUNT(*) FROM NETWORK_INCIDENTS;\n```\n\n### Verify Cortex Search Services (2)\n\n```sql\nSHOW CORTEX SEARCH SERVICES IN SCHEMA DEFAULT_SCHEMA;\n```\n\nYou should see:\n\n- ✅ CALL_TRANSCRIPT_SEARCH\n- ✅ SUPPORT_TICKET_SEARCH\n\n### Verify Applications\n\n**Notebooks**:\n\n```sql\nSHOW NOTEBOOKS IN TELCO_OPERATIONS_AI.NOTEBOOKS;\n```\n\n- ✅ 1_DATA_PROCESSING\n- ✅ 2_ANALYZE_CALL_AUDIO\n- ✅ 3_INTELLIGENCE_LAB\n\n**All verified?** ✅ Let's start using the AI features!\n\n\u003C!-- ------------------------ --\u003E\n\n## Explore the Demo\nDuration: 45\n\n### Installation Complete - Now Let's Explore!\n\nCongratulations! You've successfully deployed the NovaConnect Telco Operations AI platform. The installation phase is complete.\n\n**What you've deployed:**\n- Database with 14+ tables of telco data\n- 2 Cortex Search services for semantic search\n- 3 Cortex Analyst semantic models for natural language queries\n- 1 Snowflake CoWork Agent\n- 3 Snowflake Notebooks for data processing\n\n**What's next:**\nIn the following sections, you'll explore and interact with the AI capabilities you just deployed:\n\n| Stage | Section | What You'll Do |\n|-------|---------|----------------|\n| **1** | **AI & ML Studio** | Explore Cortex Playground and Document Processing Playground to understand LLM capabilities |\n| **2** | **Snowflake Notebooks** | Run 3 notebooks to process documents, transcribe audio, and explore AI functions |\n| | - Document Processing | Process PDFs with AI_PARSE_DOCUMENT (Notebook 1) |\n| | - Audio Analysis | Transcribe call recordings with AI_TRANSCRIBE (Notebook 2) |\n| | - Intelligence Lab | Advanced AI exploration and analytics (Notebook 3) |\n| **3** | **AI Services** | Explore the deployed Cortex Search and Cortex Analyst services |\n| **4** | **Intelligence Agent** | Chat with your telco data using the deployed agent |\n\n---\n\n## AI & ML Studio\n\n### Snowflake AI and ML Studio\n\nBefore diving into the notebooks, let's explore the **AI & ML Studio** - your one-stop shop for trying out AI functions using a user-friendly UI.\n\nNavigate to the **AI & ML** section in the Snowflake navigation bar.\n\n### Features You'll Explore\n\nThe AI and ML Studio provides access to:\n\n- **Cortex Playground** - Compare text completions across multiple LLMs\n- **Cortex Fine Tuning** - Customize large language models for specific tasks\n- **Cortex Search** - Low-latency semantic search over your data\n- **Cortex Analyst** - Text-to-SQL for business intelligence\n- **Document Processing Playground** - Explore AI_EXTRACT and AI_PARSE_DOCUMENT functions\n\n---\n\n### Cortex Playground\n\nThe Cortex LLM Playground lets you compare text completions across multiple large language models available in Cortex AI.\n\n#### Why This Matters for Unstructured Data\n\nBefore processing unstructured documents at scale, it's valuable to understand how LLMs interpret and extract information from text. The Cortex Playground provides a sandbox to:\n\n- **Test different models** - Compare how Claude, Llama, Mistral, and other models handle your specific use cases\n- **Refine your prompts** - Experiment with prompt engineering before building production pipelines\n- **Understand model capabilities** - See how models extract key information, summarize content, and answer questions\n- **Validate extraction strategies** - Test whether a model can reliably pull specific data points from unstructured text\n\nThis foundational understanding will help you design better document processing workflows when you move to the Notebooks section.\n\n![Cortex Playground](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/cortex-playground.png)\n\n#### Try it now:\n\n1. Click on **Cortex Playground** in the AI & ML Studio\n2. Select a model (e.g., `claude-4-sonnet`, `llama-3.1-70b`, `mistral-large2`)\n3. Try asking a telecommunications question:\n\n**Example prompt:**\n\n\u003E \"What are the key metrics I should monitor for 5G network performance? What factors indicate potential service degradation?\"\n\n**What you'll see:**\nThe model will suggest various factors to consider:\n\n- Network latency and jitter\n- Download/upload speeds\n- Packet loss rates\n- Handover success rates\n- Signal strength (dBm)\n- User load and capacity utilization\n\n#### Experiment with Document-Style Prompts\n\nTry pasting a sample of unstructured text and asking the model to extract specific information:\n\n**Example - Extracting from a support ticket:**\n\n\u003E \"Extract the following from this support ticket: customer issue, product mentioned, urgency level, and resolution requested.\n\u003E\n\u003E Ticket: Customer called regarding slow internet speeds on their NovaConnect Pro plan. They've been experiencing issues for 3 days and have already tried restarting their router. Customer is frustrated as they work from home and need this resolved urgently. Requesting a technician visit or account credit.\"\n\n**What you'll learn:**\n- How well the model identifies key entities\n- Whether it can handle domain-specific terminology\n- How to structure extraction prompts for consistent results\n\n**Key insight**: The prompt engineering skills you develop here translate directly to the AI_EXTRACT and AI_PARSE_DOCUMENT functions you'll use in the Notebooks.\n\n---\n\n### Document Processing Playground\n\n![Document Processing Playground](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/document-processing-playground.png)\n\nThe **Document Processing Playground** is a powerful AI tool that helps you understand how text is extracted from documents. It provides an interactive UI for testing **AI_EXTRACT** and **AI_PARSE_DOCUMENT** functions, allowing you to experiment with different extraction strategies before implementing them in production.\n\n#### Step 1: Upload Documents from Stage\n\n1. Click **Document Processing Playground** in the AI & ML Studio\n2. Click **Add from stage**\n3. Select the following:\n   - **Database**: `TELCO_OPERATIONS_AI`\n   - **Schema**: `DEFAULT_SCHEMA`\n   - **Stage**: `PDF_STAGE`\n4. Choose 1-2 PDF documents (e.g., NovaConnect help documents)\n5. Click **Open playground**\n\n![Document Selected in Playground](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/pdfs_document_playground.png)\n\n#### Step 2: Extract Information Using Questions\n\nOnce your document is loaded, you'll see three tabs: **Extraction**, **Markdown**, and **Text**.\n\nThe **Extraction** tab is where you can ask questions to pull specific information from the document.\n\n**Try creating these key-value question pairs:**\n\n- **Key**: `product_name` **Question**: `What is the name of the product or service?`\n- **Key**: `features` **Question**: `What are the key features mentioned?`\n- **Key**: `price` **Question**: `What is the pricing information?`\n- **Key**: `data_allowance` **Question**: `What is the data allowance or quota?`\n- **Key**: `contract_terms` **Question**: `What are the contract terms?`\n\nAfter entering each question, click **Add Prompt** to see the extracted results.\n\n![Extraction with Contract Terms](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/contract-terms.png)\n\n#### Step 3: Get the SQL Code\n\nOnce you've asked at least one question, the playground automatically generates SQL code:\n\n1. Click **Code Snippets** in the top right corner\n2. Review the generated SQL using AI_EXTRACT and AI_PARSE_DOCUMENT functions\n3. Click **Workspaces** to open the code in a new worksheet\n\n**Example generated code:**\n\n```sql\n-- Extract Data\nSELECT AI_EXTRACT(\n    file =\u003E TO_FILE('@TELCO_OPERATIONS_AI.DEFAULT_SCHEMA.PDF_STAGE', 'NovaConnect_Easy_360.pdf'),\n    responseFormat =\u003E PARSE_JSON('{\"schema\":{\"type\":\"object\",\"properties\":{\"contract_terms\":{\"description\":\"what are the contract terms\",\"type\":\"string\"}}}}')\n) AS extracted_data;\n\n-- Parse file (Layout mode)\nSELECT AI_PARSE_DOCUMENT(\n    TO_FILE('@TELCO_OPERATIONS_AI.DEFAULT_SCHEMA.PDF_STAGE', 'NovaConnect_Easy_360.pdf'),\n    { 'mode': 'LAYOUT', 'page_split': true }\n) AS parsed_document;\n```\n\nThis SQL code can be used to automate document processing at scale!\n\n\u003E **💡 Tip:** You'll see these functions in action in the **1_DATA_PROCESSING** notebook, which processes all 8 NovaConnect PDF documents using `AI_PARSE_DOCUMENT` with LAYOUT mode for bulk text extraction.\n\n---\n\n### Summary: What You've Learned\n\nBefore proceeding to the notebooks, you now understand:\n\n✅ **Cortex Playground** - How to test LLM capabilities and prompt engineering\n✅ **AI_PARSE_DOCUMENT** - Extract text and structure from PDFs\n✅ **AI_EXTRACT** - Pull specific fields from documents using questions\n✅ **SQL Code Generation** - Automating document processing at scale\n\n**Next**: Apply these concepts in the notebooks to process help documents and call recordings!\n\n\u003C!-- ------------------------ --\u003E\n\n## Document Processing\n\n### Overview\n\nIn this section, you'll process unstructured documents using Cortex AI functions through Notebook 1.\n\n**What You'll Process:**\n\n- 📄 **8 Help Documents** (PDFs) - Product guides, troubleshooting documents, and service information\n\n**Cortex AI Functions You'll Use:**\n\n- **AI_PARSE_DOCUMENT** - Extract all text from PDFs preserving layout\n- **AI_COMPLETE** - Generate structured extractions using LLMs\n- **AI_SENTIMENT** - Analyze emotional tone in customer communications\n\n### Open Notebook 1\n\nNavigate to **AI & ML Studio** → **Notebooks** → **1_DATA_PROCESSING**\n\n### What the Notebook Demonstrates\n\n**PDF Processing**:\n\n- Parse help documents with AI_PARSE_DOCUMENT\n- Extract product features, pricing, and data allowances\n- Create searchable content from unstructured PDFs\n\n**Key Tables Created by This Notebook**:\n\nAfter running this notebook, you'll have processed PDFs ready for search and analysis.\n\n### Follow the Notebook\n\nThe notebook contains **detailed instructions and explanations** for each step. Simply:\n\n1. Click **Start** to begin the notebook session\n2. Read through each cell's markdown explanations\n3. **Run All** or execute cells one by one\n4. Observe the outputs and interactive visualizations\n\n**Time**: 10-15 minutes to run through all cells.\n\n\u003C!-- ------------------------ --\u003E\n\n## Audio Analysis\n\n### Overview\n\nProcess customer call recordings using **AI_TRANSCRIBE** to convert speech to text, then analyze the content for sentiment and insights.\n\n**What You'll Process:**\n\n- 🎙️ **25 Customer Support Calls** (MP3 audio files)\n- 🗣️ **Speaker Identification** - Separate customer and agent comments\n- 💭 **Sentiment Analysis** - Measure emotional tone throughout the call\n- 📝 **Call Summarization** - Generate AI summaries of each call\n\n### Open Notebook 2\n\nNavigate to **AI & ML Studio** → **Notebooks** → **2_ANALYZE_CALL_AUDIO**\n\n### What the Notebook Demonstrates\n\n**AI_TRANSCRIBE** - Converts MP3 audio to timestamped text:\n\n- Generates full transcripts with speaker identification\n- Preserves timestamps for navigation\n- Processes multi-speaker conversations\n\n**AI_SENTIMENT** - Analyzes emotional tone:\n\n- Scores sentiment segment by segment\n- Identifies positive, negative, and neutral sections\n- Tracks sentiment trends over the call\n\n**AI_COMPLETE** - Summarizes calls:\n\n- Creates concise call summaries\n- Identifies key issues discussed\n- Extracts action items and resolutions\n\n### Key Tables Updated by This Notebook\n\nAfter running this notebook, you'll have:\n\n- **CUSTOMER_CALL_TRANSCRIPTS** - Full transcripts with summaries\n- Updated sentiment analysis for calls\n\n💡 **Note**: The `CALL_TRANSCRIPTS` table with sample data is pre-loaded during initial deployment for the search services to work immediately.\n\n### Follow the Notebook\n\nThe notebook includes:\n\n- Audio file listings and metadata\n- Transcription with AI_TRANSCRIBE\n- Sentiment analysis with AI_SENTIMENT\n- Call summarization with AI_COMPLETE\n- Interactive visualizations\n\nSimply run through the cells to see how audio becomes searchable, analyzable data.\n\n**Time**: 10-15 minutes to complete.\n\n\u003C!-- ------------------------ --\u003E\n\n## Intelligence Lab\n\n### Overview\n\nNotebook 3 provides advanced analytics and visualizations combining all your processed data.\n\n### Open Notebook 3\n\nNavigate to **AI & ML Studio** → **Notebooks** → **3_INTELLIGENCE_LAB**\n\n### What the Notebook Demonstrates\n\n**Cross-Domain Analysis**:\n\n- Correlating network issues with customer complaints\n- Identifying patterns in customer churn\n- Regional performance comparisons\n- Agent performance analytics\n\n**Visualizations**:\n\n- Network performance dashboards\n- Customer sentiment trends\n- Capacity utilization charts\n- Call center metrics\n\n### Follow the Notebook\n\nThis notebook brings together insights from all data sources for comprehensive operational intelligence.\n\n**Time**: 10-15 minutes to complete.\n\n\u003C!-- ------------------------ --\u003E\n\n## Cortex Search Services\n\n### Overview\n\nThe deployment scripts created **2 intelligent search services** that make your telecommunications data instantly accessible using semantic search. This is the foundation for RAG (Retrieval Augmented Generation) applications.\n\n**Why Search Services Matter:**\n\n- Traditional keyword search fails with conversational content\n- Semantic search understands meaning and context\n- Critical for AI agents to find relevant information quickly\n- Powers the Telco Operations agent you'll use later\n\n**What's Deployed:**\n\n1. **CALL_TRANSCRIPT_SEARCH** - Search customer call transcripts by conversation content\n2. **SUPPORT_TICKET_SEARCH** - Search support tickets by issue description\n\n### Test the Search Services\n\n1. Navigate to **AI & ML Studio** → **Cortex Search**\n2. Select **CALL_TRANSCRIPT_SEARCH** service\n3. Try a search: \"network connectivity problems\"\n4. Observe:\n   - Semantic search results (not exact match)\n   - Ranked by relevance\n   - Attributes returned (CALL_ID, SPEAKER_ROLE, SENTIMENT_SCORE)\n\n### Search Service Details\n\n| Service | Purpose | Search Column | Attributes |\n|---------|---------|---------------|------------|\n| CALL_TRANSCRIPT_SEARCH | Customer call transcripts | SEGMENT_TEXT | CALL_ID, SPEAKER_ROLE, SENTIMENT_SCORE, CALL_TIMESTAMP |\n| SUPPORT_TICKET_SEARCH | Support tickets | DESCRIPTION | TICKET_ID, CUSTOMER_ID, CATEGORY, STATUS, PRIORITY |\n\n\u003C!-- ------------------------ --\u003E\n\n## Cortex Analyst\n\n### Overview\n\n**Cortex Analyst** enables natural language querying of structured data using semantic models. Instead of writing SQL, users ask questions in plain English and Cortex Analyst generates the SQL automatically.\n\n**What's Deployed:**\n\n- 📊 **3 Semantic Models** - Network performance, infrastructure capacity, customer feedback\n- 🔍 **Natural Language to SQL** - Ask questions, get SQL and results\n- 📈 **Verified Queries** - Pre-tested queries for common questions\n\n### Semantic Model 1: Network Performance\n\n**Purpose**: Query network metrics across cell towers and regions\n\n**Key Dimensions**:\n- TOWER_ID, TOWER_NAME, REGION, NETWORK_TYPE\n\n**Key Facts**:\n- AVG_LATENCY_MS, AVG_DOWNLOAD_SPEED_MBPS, PACKET_LOSS_PCT\n- CALL_DROP_RATE_PCT, HANDOVER_SUCCESS_RATE_PCT, AVAILABILITY_PCT\n\n**Example Questions**:\n\n1. \"Which towers have high latency issues?\"\n2. \"What are the best performing 5G towers by download speed?\"\n3. \"Show me network availability by region\"\n4. \"Which regions have the highest call drop rates?\"\n\n### Semantic Model 2: Infrastructure Capacity\n\n**Purpose**: Analyze bandwidth utilization and capacity planning\n\n**Key Dimensions**:\n- TOWER_ID, TOWER_NAME, REGION, EQUIPMENT_STATUS\n\n**Key Facts**:\n- TOTAL_BANDWIDTH_GBPS, USED_BANDWIDTH_GBPS, UTILIZATION_PCT\n- EXPECTED_GROWTH_PCT, UPGRADE_RECOMMENDED\n\n**Example Questions**:\n\n1. \"Which towers are operating at over 80% capacity?\"\n2. \"Which towers need infrastructure upgrades?\"\n3. \"Which towers will run out of capacity in the next 6 months?\"\n4. \"Show me 5G towers with upgrade recommended\"\n\n### Semantic Model 3: Customer Feedback\n\n**Purpose**: Analyze customer sentiment, complaints, and churn\n\n**Key Dimensions**:\n- REGION, FEEDBACK_TYPE, CUSTOMER_SEGMENT, PLAN_TYPE\n\n**Key Facts**:\n- COMPLAINT_COUNT, AVG_SENTIMENT_SCORE, NETWORK_ISSUE_COUNT\n- MONTHLY_REVENUE, TENURE_MONTHS, IS_CHURNED\n\n**Example Questions**:\n\n1. \"Which regions have the most customer complaints?\"\n2. \"What is the trend in customer sentiment over the past month?\"\n3. \"What are the top reasons for customer churn?\"\n4. \"Show me customers at risk of churning\"\n\n### Explore Cortex Analyst in the UI\n\n1. From the navigation bar, click **AI & ML** → **Studio**\n2. Click on **Cortex Analyst**\n3. The semantic models are available via the stage `@TELCO_OPERATIONS_AI.CORTEX_ANALYST.CORTEX_ANALYST`\n\n### Key Insights\n\n✅ **Semantic Models** bridge business language and database schemas\n✅ **Verified Queries** provide pre-tested SQL for common questions\n✅ **Natural Language** enables non-technical users to query data\n✅ **Multiple Models** allow domain-specific analysis\n\n**Next**: These semantic models become tools for the Snowflake CoWork Agent!\n\n\u003C!-- ------------------------ --\u003E\n\n## Intelligence Agent\n\n### Overview: The Telco Operations AI Agent\n\nThe **Telco Operations AI Agent** is your AI-powered telecommunications operations assistant that combines multiple data sources, search capabilities, and analytical tools to provide comprehensive insights about network performance, customer experience, and operational efficiency.\n\n**What the Agent Can Do**:\n\n- 📊 Analyze network performance metrics and identify issues\n- 🏗️ Monitor infrastructure capacity and recommend upgrades\n- 💬 Search customer call transcripts for specific topics\n- 📧 Search support tickets for issue patterns\n- 📈 Track customer sentiment and churn risk\n\nThis agent represents the **culmination of all the previous work** - it brings together the unstructured data processing, search services, and semantic models into one conversational interface.\n\n---\n\n### Access the Agent\n\n![Snowflake CoWork](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/snowflake-intelligence-ui.jpg)\n\n1. Navigate to **AI & ML Studio** → **Snowflake CoWork** in your Snowflake account\n2. Select the **Telco Operations AI Agent**\n3. The agent will automatically select the right tools based on your question\n\n**Location**: `SNOWFLAKE_INTELLIGENCE.AGENTS.\"Telco Operations AI Agent\"`\n\n![Intelligence Tab Navigation](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/intelligence-navigation.png)\n\n---\n\n### Agent Architecture: 5 Powerful Tools\n\nThe Telco Operations AI Agent has access to **5 different tools** that it automatically orchestrates based on your questions:\n\n#### Cortex Analyst Tools (3)\n\n**1. network_performance**\n\n- Network performance metrics for cell towers across regions\n- Latency, speed measurements, user activity, service quality\n- Uses: `network_performance.yaml` semantic model\n\n**2. Customer_Feedback**\n\n- Customer feedback analytics with sentiment analysis\n- Feedback categorization and customer details including churn\n- Uses: `customer_feedback.yaml` semantic model\n\n**3. Infrastructure_Capacity**\n\n- Infrastructure capacity metrics for telecom towers\n- Bandwidth utilization, equipment status, capacity planning\n- Uses: `infrastructure_capacity.yaml` semantic model\n\n#### Cortex Search Tools (2)\n\n**4. CALL_TRANSCRIPTS**\n\n- Search customer call transcripts for specific issues or keywords\n- Includes sentiment scores and speaker identification\n- Service: `CALL_TRANSCRIPT_SEARCH`\n\n**5. SUPPORT_TICKETS**\n\n- Search support tickets for issue tracking and resolution\n- Includes priority, category, and status information\n- Service: `SUPPORT_TICKET_SEARCH`\n\n---\n\n### What the Agent Can Do\n\n#### 1. Network Performance Analysis\n\n**Try asking:**\n\n\u003E \"Which regions have the highest network latency issues?\"\n\nThe agent will:\n\n- Query the network_performance semantic model\n- Analyze latency metrics by region\n- Rank regions by average latency\n- Show you towers that need attention\n\n**Behind the scenes:** Uses the `network_performance` Cortex Analyst tool to generate and execute SQL.\n\n#### 2. Capacity Planning\n\n**Try asking:**\n\n\u003E \"Show me 5G towers operating above 80% capacity\"\n\nThe agent will:\n\n- Query infrastructure capacity data\n- Filter for 5G towers with high utilization\n- Show capacity exhaustion timelines\n- Recommend upgrades where needed\n\n**Data sources:** Infrastructure capacity metrics with utilization percentages and growth projections.\n\n#### 3. Customer Call Analysis\n\n**Try asking:**\n\n\u003E \"Find calls mentioning network connectivity problems\"\n\nThe agent will:\n\n- Search call transcripts semantically\n- Find conversations about connectivity issues\n- Show sentiment scores for matching calls\n- Identify patterns in customer complaints\n\n**Demonstrates:** Semantic search across transcribed call recordings.\n\n#### 4. Customer Churn Analysis\n\n**Try asking:**\n\n\u003E \"Which customer segments have the highest churn risk?\"\n\nThe agent will:\n\n- Analyze customer feedback and churn data\n- Segment by customer type and plan\n- Identify at-risk customers\n- Correlate with complaint patterns\n\n#### 5. Cross-Domain Insights\n\n**Try asking:**\n\n\u003E \"Are network issues correlated with negative customer sentiment?\"\n\nThe agent will:\n\n- Query both network performance and customer feedback\n- Correlate latency spikes with complaint volumes\n- Identify regions with both technical and satisfaction issues\n- Provide actionable insights\n\n#### 6. Cross-Domain Analysis\n\n**Try asking:**\n\n\u003E \"Are network issues correlated with negative customer sentiment?\"\n\nThe agent will:\n\n- Query both network performance and customer feedback\n- Correlate latency spikes with complaint volumes\n- Identify regions with both technical and satisfaction issues\n- Provide actionable insights\n\n---\n\n### Sample Conversation Flow\n\nHere's an example conversation demonstrating the agent's capabilities:\n\n**You:** \"Which regions have the highest network latency issues?\"\n\n**Agent:** *Queries network performance, shows regions ranked by average latency*\n\n**You:** \"Show me 5G towers operating above 80% capacity\"\n\n**Agent:** *Queries infrastructure capacity, lists overloaded towers with utilization %*\n\n**You:** \"Find calls mentioning network connectivity problems\"\n\n**Agent:** *Searches call transcripts, returns matching conversations with sentiment*\n\n**You:** \"What are the top 3 customer complaints in October 2025?\"\n\n**Agent:** *Queries customer feedback, shows complaint categories ranked*\n\n---\n\n### Sample Questions to Try\n\nHere are the sample questions configured in the agent:\n\n![Agent Sample Questions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/intelligence-sample-questions.jpg)\n\n1. \"Which regions have the highest network latency issues?\"\n2. \"Show me 5G towers operating above 80% capacity\"\n3. \"Find calls mentioning network connectivity problems\"\n4. \"What are the top 3 customer complaints in October 2025?\"\n5. \"Find support tickets about billing issues\"\n6. \"Which customer segments have the highest churn risk?\"\n7. \"Are network issues correlated with negative customer sentiment?\"\n8. \"Show me regions with both high latency and customer complaints\"\n9. \"What percentage of calls mention competitor names?\"\n\n![Agent Testing Example](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-query-response-1.png)\n\n![Agent Testing Example 2](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-query-response-2.png)\n\n---\n\n### Editing and Understanding the Agent\n\nAfter using the agent, explore its configuration:\n\n1. Navigate to **AI & ML Studio** → **Agents**\n2. Click on **Telco Operations AI Agent**\n3. Click **Edit** to view:\n   - **Sample Questions**: The questions you just tried\n   - **Tools**: All 5 tools available to the agent\n   - **Orchestration Instructions**: How the agent decides which tools to use\n   - **Access Control**: Who can use the agent\n\n![Create Agent Button](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/create-agent-button.png)\n\n### Agent Configuration Details\n\nWhen configuring the agent, you'll see these key sections:\n\n**Agent Description:**\n\n![Describe Agent](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-description-form.png)\n\n**Semantic Model YAML Files:**\n\nThe agent uses 3 semantic models located in the CORTEX_ANALYST stage:\n\n![YAML Files](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/semantic-model-yaml-files.jpg)\n\n**Add Tools with Detailed Descriptions:**\n\n![Detailed Description](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/tool-description-generate.png)\n\n**All 3 Semantic Model Tools:**\n\n![All 3 Semantic Model Tools](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/semantic-model-tools-added.png)\n\n**Cortex Search Service Configuration:**\n\n![Call Transcripts Search](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/cortex-search-transcripts-config.jpg)\n\n![Search Services](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/cortex-search-services-list.jpg)\n\n**Test Your Agent:**\n\nThe Test Agent interface is where you can interact with your AI assistant in real-time before publishing it. Here you can:\n\n- **Ask natural language questions** about network performance, customer feedback, and infrastructure capacity\n- **Verify tool selection** - watch which tools (Cortex Analyst semantic models or Cortex Search services) the agent chooses to answer each question\n- **Review SQL generation** - see the actual SQL queries generated by Cortex Analyst for structured data questions\n- **Test multi-tool queries** - try complex questions that require the agent to combine data from multiple sources\n- **Refine responses** - iterate on your tool descriptions and instructions to improve answer quality\n- **Debug issues** - identify when the agent selects the wrong tool or misunderstands a question\n\nTry questions like: *\"What regions have the highest call drop rates?\"* or *\"What are customers saying about 5G coverage?\"*\n\n![Test Agent](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-test-interface.png)\n\n**Configure Sample Questions:**\n\n![Agent Questions](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/agent-sample-questions.jpg)\n\n---\n\n### Data Coverage\n\n#### Network Data\n- **Multiple Regions**: Geographic coverage across all operational areas\n- **Network Types**: 4G and 5G towers\n- **Performance Metrics**: Latency, speed, packet loss, availability\n\n#### Customer Data\n- **Customer Segments**: Consumer, Business, Enterprise\n- **Plan Types**: Premium 5G, Standard 4G, Business Plan\n- **Churn Analysis**: Reasons, at-risk identification\n\n#### Interaction Data\n- **25 Call Recordings**: Transcribed with AI_TRANSCRIBE\n- **Support Tickets**: Categorized by type and priority\n- **Feedback**: Sentiment analyzed by channel and region\n\n---\n\n### Configure Snowflake CoWork Settings\n\nTo customize the appearance of your Intelligence interface:\n\n1. Navigate to **AI & ML** → **Agents** → **Snowflake CoWork** tab\n2. Click **Open Settings**\n\n![Intelligence Settings Panel](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/intelligence-settings.png)\n\n**Customize the Interface:**\n\n- **Display Name**: \"NovaConnect Intelligence\" or your organization name\n- **Description**: Brief description of what the agent can help with\n- **Welcome Message**: Greeting users see when starting a conversation\n- **Color Theme**: Match your brand colors\n\n**Brand Your Intelligence with Logos:**\n\nUse the NovaConnect logos to brand your interface:\n\n![NovaConnect Full Logo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/novaconnect-logo.png)\n\n![NovaConnect Compact Logo](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence/novaconnect-logo-icon.png)\n\n---\n\n### Tips for Best Results\n\n#### Ask Specific Questions\n\n✅ **Good**: \"Which 5G towers have latency above 25ms?\"\n❌ **Too vague**: \"Tell me about the network\"\n\n#### Use Follow-up Questions\n\nThe agent maintains context, so you can:\n\n1. Ask initial question\n2. Drill deeper on specific findings\n3. Request different visualizations\n4. Ask for comparisons\n\n---\n\n### Understanding Agent Responses\n\n#### When You See Charts\n\nThe agent automatically generates visualizations based on the orchestration instruction to convert to logs when comparing measures.\n\n#### When You See \"Checking multiple sources...\"\n\nThe agent is:\n\n- Querying semantic models\n- Searching search services\n- Combining results from multiple tools\n\nThis ensures comprehensive, validated answers.\n\n---\n\n### Data Limitations & Disclaimers\n\n#### Synthetic Data\n\n- **NovaConnect**: Entirely fictional telecommunications company\n- **All metrics**: Synthetic but industry-realistic\n- **Customer data**: Generated for demonstration purposes\n\n#### Purpose\n\nThis is a **demonstration environment** showing telecommunications AI capabilities. **Do not make actual business decisions based on this data!**\n\n---\n\n## Customize Regions (Optional)\nDuration: 5\n\n### Make the Demo Your Own\n\nThe sample data uses Malaysian regions by default (Kuala Lumpur, Selangor, Penang, etc.). If you'd like to customize the demo to use regions relevant to your geography, we've included a customization script.\n\n### Running the Customization Script\n\n1. Navigate to the SQL script in your Git repository:\n\n```sql\n-- Open from Snowflake UI or run directly\n@TELCO_AI_REPO/branches/main/assets/sql/99_customize_regions.sql\n```\n\n2. Or create a new SQL worksheet and copy the script from:\n   `assets/sql/99_customize_regions.sql`\n\n### Available Presets\n\nThe script includes several preset configurations you can uncomment:\n\n| Preset | Example Regions |\n|--------|-----------------|\n| **US Cities** | New York Metro, Los Angeles, Chicago, Houston, Phoenix |\n| **European** | London, Paris, Berlin, Madrid, Rome, Amsterdam |\n| **Generic** | Region Alpha, Region Beta, Region Gamma, etc. |\n\n### Custom Mapping\n\nTo create your own mapping, modify the `INSERT INTO REGION_MAPPING` statements:\n\n```sql\nINSERT INTO REGION_MAPPING VALUES\n    -- (Original Region, Your New Region, Original Prefix, Your New Prefix)\n    ('Kuala Lumpur', 'Your City Name', 'KL', 'YCN'),\n    ('Selangor', 'Another Region', 'SEL', 'AR'),\n    -- ... add more as needed\n```\n\n### What Gets Updated\n\nThe script updates the following tables:\n- `NETWORK_PERFORMANCE` - Tower regions and names\n- `INFRASTRUCTURE_CAPACITY` - Tower regions and names  \n- `CUSTOMER_FEEDBACK_SUMMARY` - Feedback regions\n\nAfter running the customization, your agent queries will return results using your custom region names!\n\n---\n\n## Conclusion\n\n### What You've Accomplished\n\nIn this quickstart, you've built a comprehensive AI-powered telecommunications operations platform using Snowflake's Cortex AI capabilities. You started by processing unstructured data—extracting insights from PDF help documents using Cortex Document Processing, transcribing customer call recordings with AI_TRANSCRIBE, and analyzing sentiment across thousands of data points. This transformed raw, unstructured telecommunications content into structured, queryable data that forms the foundation of intelligent applications.\n\nYou then created the AI services layer that makes this data accessible and actionable. Cortex Search services enable semantic search across call transcripts and support tickets—finding relevant information based on meaning rather than keywords. Cortex Analyst semantic models allow natural language queries against structured network and customer data, automatically generating SQL from plain English questions. Together, these services power the intelligence layer that connects users to insights without requiring technical expertise.\n\nFinally, you deployed and explored the Snowflake CoWork Agent—the \"Telco Operations AI Agent\" that orchestrates all of these capabilities through a conversational interface. The agent seamlessly combines search services and semantic models to answer complex operational questions and generate visualizations. This demonstrates how modern AI platforms can unify structured and unstructured data analysis, enabling faster decision-making and deeper insights across telecommunications operations use cases.\n\n### Resources\n\n- **Repository**: [GitHub - Telco AI Assistant](https://github.com/Snowflake-Labs/sfguide-build-an-ai-assistant-for-telco-with-aisql-and-snowflake-intelligence)\n- **Documentation**: See README.md in repository\n\n---\n\n**Ready to build AI-powered applications with Snowflake Cortex?** Start experimenting with your own data today!\n\n### Related Resources\n\n- [Snowflake AI and ML Features](https://docs.snowflake.com/guides-overview-ai-features)\n- [Snowflake CoWork Overview](https://docs.snowflake.com/en/user-guide/snowflake-cortex/snowflake-intelligence)\n- [Snowflake for Telecommunications](https://www.snowflake.com/en/solutions/industries/telecommunications/)\n\n---\n\n## Re-deploying / Reset\n\nSince the Git repo is in a separate database, you can easily reset:\n\n```sql\n-- Drop the main database (Git repo stays safe!)\nDROP DATABASE IF EXISTS TELCO_OPERATIONS_AI;\nDROP DATABASE IF EXISTS SNOWFLAKE_INTELLIGENCE;\n\n-- Fetch latest code\nALTER GIT REPOSITORY TELCO_AI_LAB.GIT_REPOS.TELCO_AI_REPO FETCH;\n```\n\nThen re-run deployment scripts (01-05) from the Git Repositories UI as described in Step 3.\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-dc047c9775","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-374de92638",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-0bf2be8e3b","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-02-03",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-5660f9ca9a","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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