Video
SNOWFLAKE: WHERE AI-POWERED DATA MEETS DATA POWERED AI
Discover how Financial Services organizations are building AI with Snowflake
LEADING FINANCIAL SERVICES ORGANIZATIONS BANK ON SNOWFLAKE
HOW SNOWFLAKE MAKES ENTERPRISE AI EASY, TRUSTED AND EFFICIENT
Use the LLM that fits your needs
Access industry-leading models, without worrying about managing GPU infrastructure or moving data out of Snowflake. Securely fine-tune models using your own data.
Deliver data-backed results
Trusted AI-insights require access to your structured and unstructured data ecosystem. Chat with structured data using our high accuracy text-to-SQL service. Retrieve info from documents with hybrid search.
Protect your data
Safeguard your data and AI with unified governance controls. Customer Data is never used for training broadly available models or services.
Deploy your AI applications
Build UI interfaces for your AI apps using Streamlit, our open-source Python library that makes it easy to build, deploy and share custom applications without moving data or code externally.
Simplify your architecture
Managing your data and AI stack is complex. Snowflake offers tightly integrated features accessible in both no-code or code options – for all types of users.
Optimize for ROI
Benefit from Snowflake platform enhancements and effectively manage your Snowflake spend with built-in cost management capabilities.
The Foundations of an Enterprise AI Strategy
Access to your structured and unstructured data
Access to industry-leading LLMs
Index, search and retrieval capabilities to connect the LLMs to your data
AI-powered tools to accelerate build
Application framework to build, deploy and share your AI apps
ENTERPRISE AI FOR ALL TYPES OF USERS
C-Suite
Use fully-managed AI infrastructure and capabilities across the enterprise. Guard against IP vulnerabilities while gaining visibility into access, usage and outcomes with a single layer of security controls across your data and AI landscape.
Business Leaders
Build a more productive, efficient organization with self-service data and AI features available in no-code or code options for both business users and developers. Embed AI capabilities across workflows to modernize customer experiences and create new revenue opportunities.
Developers
Easily leverage the same fully-integrated tools and features — from data ingestion and advanced data warehousing to AI implementation — without having to secure or manage additional integrations. Use LLMs out of the box or fine-tune existing models simply via a function.
FINANCIAL SERVICES GENERATIVE AI USE CASES
Self-service analytics
Use natural language to query against your data. With high-accuracy text-to-SQL, business users can get back sophisticated business intelligence that they can trust. This means self-service analytics across financial services workflows, such as portfolio analytics, risk management, customer 360, and underwriting.
Conversational assistants
Augment employee performance with RAG-enabled assistants that uncover insights, transform knowledge management and help the organization run more smoothly. Tap into unstructured data by allowing users to search documents and other multimedia information easily through chat — including claims, call logs, research and technical reports.
Text analysis and generation
Rapidly unearth subtle signals from large volumes of unstructured data. Sift through SEC filings and fund prospectuses for important insights. Turn client notes and conversations into Next Best Action. Or quickly analyze and categorize M&A deal terms and extract sentiment from news.
Self-service analytics with Snowflake Cortex Analyst
HEAR FROMINDUSTRY LEADERS
Watch our interview with Aman Thind, EVP, Global Chief Architect for State Street. State Street Alpha is applying AI across several use cases, from building smarter portfolios and improving data quality to optimizing manual, error-prone middle-office workflows, offering institutional investors the ability to develop portfolio insights — all from a natural language interface.
SnowflakeFAQ
What is generative AI?
Generative AI is a form of artificial intelligence that creates content from a series of natural language prompts by relying on generative AI models trained on unstructured data. These large language models (LLMs) include GPT-4, LLaMA, Mixtral, Gemini, and Snowflake’s own Snowflake Arctic.
What is the difference between generative AI and traditional AI?
Generative AI creates new content, such as text, code, images, or synthetic data, while traditional AI analyzes unstructured data to provide analysis, predictions or make decisions. Generative AI typically focuses on unstructured data for training compared to large structured datasets for traditional AI.
How does Snowflake bring access to industry-leading LLMs?
Snowflake offers users flexibility and optionality when it comes to accessing industry-leading foundational, commercial or open source models. For select foundational models, Snowflake Cortex AI is Snowflake’s fully-managed service that hosts and serves pre-loaded, on-demand AI models, LLMs and vector functions accessible via SQL or Python. Models include Llama 3.1 and Llama 3.2, Reka Core, Mistral-large, Gemma-7b, Snowflake Arctic and more.
For other commercial or open source models, Snowpark Container Services (GA) is Snowflake’s fully-managed container offering designed to facilitate the deployment, management and scaling of containerized applications, including models from Nvidia, Landing AI or other open source models on Hugging Face. Users can also access commercial LLMs listed on Snowflake Marketplace via Snowpark Container Services.
What models are currently in Snowflake Cortex AI?
Snowflake Cortex AI hosts and serves pre-loaded models from AI21 Labs, Nvidia, Meta, Mistral AI, Reka and more. Snowflake Cortex AI also hosts Snowflake’s own LLM, Snowflake Arctic.
What AI-powered tools and features does Snowflake provide?
Snowflake offers AI-powered tools and features that help drive user productivity and efficiency. These include:
Cortex Analyst enables business users to interact with data using natural language, helping them to find answers faster, self-serve insights and save valuable time. Cortex Analyst is powered by state-of-the-art LLMs like Meta LLaMA-3 and Mistral-Large.
Cortex Search enables users to build and deploy retrieval augmented generation (RAG) applications. Cortex Search offers state-of-the-art semantic and lexical search over your text data.
Document AI leverages Snowflake Arctic-TILT to extract text, table values and handwritten content from PDFs and other unstructured documents. Using Document AI, users can ask questions about documents, including claims reports, investor reports, SEC filings, prospectuses and more.
How should I think about security and governance?
Snowflake’s serverless inference architecture is designed with security at the forefront. Snowflake’s AI services are fully contained within the Snowflake secure deployment boundary. Key features about Snowflake’s governance include:
Secure – AI features adhere to the standard security program for the Snowflake Service, designed to protect the confidentiality and integrity of your data
No training on your data – Snowflake never uses Customer Data to train AI models made available to our customer base
No requests to third-party services – your data never leaves Snowflake when you use an AI feature; all data is kept within the Snowflake security perimeter
No commingling of data – AI services isolate Customer Data at the software level, so no Customer Data is ever mixed with that of another Snowflake customer
