A ready-to-run conversational AI application that guides users to relevant knowledge, surfaces insights from multiple content sources, and applies business context and reasoning to deliver clear, actionable answers across structured and unstructured data.

• Targeted Outcomes
• Faster access to answers by unifying search, retrieval, and reasoning across all knowledge sources.
• Better-informed decision-making through AI-generated insights enriched with business context and data.
• Greater knowledge reuse and consistency across teams by centralizing how information is discovered.
• Higher productivity potential as both technical and non-technical users can get immediate, more accurate responses.

Make your data AI-ready with production-ready data pipelines using OpenFlow that ingest data into Snowflake from one or more source systems, with comprehensive governance and observability controls.

• Targeted Outcomes
• Hardened, production-ready pipelines on OpenFlow with secure, seamless connectivity across database, streaming, and SaaS systems
• Unified data pipelines with native Snowflake connectivity on a governed foundation enabling scalable analytics and AI
• Faster analytics and real-time decisions with fresh data that closes the event-to-insight gap

Helps customers implement a production-ready "intelligent retrieval and discovery" agent across knowledge assets that helps teams find relevant content, case studies, or answers faster through AI-powered search.

• Targeted Outcomes
• Extract value from your unstructured data by allowing rapid search, retrieval, and summarization across your documentation.
• Democratize access to data by allowing non-technical users to focus on their data insights.
• Develop best practices on data ingestion for search, tuning, and evaluation. All while enabling your resources.

Helps customers implement a production-ready "talk to your data" agent for structured data, to drive adoption of AI-assisted analysis.

• Targeted Outcomes
• Driving operational efficiencies and reducing the number of ad hoc data and dashboard/reporting requests, freeing up capacity for higher value add activities.
• Accelerates adoption of GenAI with best practices and guidance on latest product capabilities.
• Democratize access to data by allowing non-technical users to focus on their data insights.

Streamline claims from First Notice of Loss (FNOL) to settlement with automated workflows, document intelligence, severity and exposure prediction, and faster, more accurate adjuster decisions.

• Targeted Outcomes
• Increase claims throughput by enabling teams to handle more claims and accelerate settlements
• Reduce operational waste by eliminating issue-then-reverse denial cycles and avoidable rework
• Improve risk controls by detecting fraud earlier and reducing adjudication errors
• Strengthen customer outcomes through faster resolution, better retention, and higher NPS scores

AI-powered churn prevention solution that unifies customer data, predicts churn risk, explains key risk drivers, and recommends next-best actions for retention teams.

• Targeted Outcomes
• Reduce preventable churn by improving save rates and enabling earlier customer intervention
• Protect recurring revenue by increasing customer lifetime value and retaining high-value accounts
• Improve team efficiency by focusing retention, service, and account teams on the highest-risk customers
• Expand growth potential by improving customer experience, loyalty, and expansion opportunities

Transform healthcare claims operations with AI agents that automate intake, clinical review, coding validation, and payment integrity while keeping claims teams in control.

• Targeted Outcomes
• Accelerate claims processing and reimbursement
• Reduce manual reviews; increase auto-adjudication rates
• Improve payment accuracy and FWA detection
• Improve provider satisfaction and member experience

AI-powered document processing combines extracted insights with enterprise data to create standardized, validated records, prioritize exceptions, and preserve traceable evidence./p>

• Targeted Outcomes
• Accelerate clinical document review, abstraction, and coding workflows
• Reduce manual chart review and repetitive data extraction
• Improve consistency and accuracy of clinical data and codes
• Preserve source evidence for auditability and reviewer confidence
• Standardize documentation processing across teams and facilities

Help teams proactively identify care gaps, validate quality measure evidence, and detect emerging clinical risks using governed, AI-powered insights across patient and population data.

• Targeted Outcomes
• Accelerates clinical quality decisions with evidence-backed insights and more consistent performance visibility
• Enables faster action by surfacing care gaps and performance trends before delayed reporting cycles
• Improves quality impact by strengthening performance, compliance readiness, and patient outcomes

An AI-Ready Customer 360 solution that builds unified profiles, trusted identities, governed data, and AI-powered customer insights to improve personalization, retention, and growth.

• Targeted Outcomes
• Improve retention, personalization, and revenue expansion
• Unify customer data and reduce cross-functional silos
• Act faster on real-time customer signals
• Deliver timely, relevant offers and service
• Create accurate identities and reduce duplicate records

AI-powered predictive maintenance transforms fragmented asset data into governed, AI-ready intelligence, enabling faster, more actionable maintenance decisions.

• Targeted Outcomes
• Reduce unplanned downtime and improve asset availability
• Lower maintenance costs and avoid unnecessary repairs
• Accelerate root-cause resolution and restore operations faster
• Increase asset performance, reliability, and useful life

Transform unstructured documents into structured insights by ingesting, classifying, and extracting data at scale within Snowflake, powered by AI with confidence scoring and human review.

• Targeted outcomes
• Reduce document processing time by automating extraction, classification, and routing of high-volume documents.
• Improve analyst productivity by shifting teams from manual data entry to exception review and decision-making.
• Increase accuracy and consistency by standardizing outputs, applying confidence scoring, and flagging items that need human review.

Data harmonization at scale to match, deduplicate, and unify records across systems without moving data using hybrid AI with confidence scoring, human review, and a trusted golden record output.

• Targeted outcomes
• Replaces months-long manual matching exercises with automation
• Eliminates need for third-party matching tools, external ETL, and data movement; reduces manual labor for record reconciliation.
• Clean, deduplicated data improves analytics accuracy, customer experience, and downstream decision-making.

Operationalize AI/ML solutions with security hardening, evaluation frameworks, CI/CD integration, monitoring, and governance to ensure reliable, scalable production deployment and business visibility.

• Targeted outcomes
• Hardens select AI/ML solutions for secure, reliable, and scalable production deployment.
• Integrates evaluation, CI/CD, monitoring, and governance into repeatable operating workflows.
• Improves visibility into model performance and accuracy.

Migrate or build models and pipelines to a Snowflake-native, scalable, and governed architecture delivering a production-ready ML foundation.

• Targeted outcomes
• Migrates models and pipelines into a Snowflake-native, scalable, and governed ML architecture.
• Eliminates fragmented tooling, external infrastructure, and unnecessary data movement across ML workflows.
• Production-ready ML foundation improves deployment consistency, governance, scalability, and time to value.

Series of workshops designed to provide an estimate of effort, high-level timeline, and set of recommendations for migrating a single data warehouse platform entirely, or targeted data workloads to Snowflake; with a review of opportunities to leverage automation for database code conversion, data migration, and data validation.

• Targeted Outcomes
• Clarify scope and alignment across stakeholders
• Reduce architectural ambiguity
• Better understand effort and resource requirements
• Reduce migration risks and unknowns
• Identify migration accelerator opportunities

Production pilot for a set of business users or other consumption methods to move live reporting from the legacy system to Snowflake.

• Targeted Outcomes
• Potential to address other current issues such as data security / privacy early in the migration.
• Identify potential data differences due to data types, rounding, space issues, etc.
• Implement necessary change management early based upon validation results.

A team of experienced migration experts to help guide and enable customer teams during the initial weeks of a DIY migration to Snowflake through key support activities.

• Targeted Outcomes
• Accelerate migration by enabling internal team with Snowflake experts and accelerators.
• Enable and teach customer teams on migration process and accelerators.
• Identify potential risks early in the project.
• Review the migration plan for potential issues and recommend approach.
• Identify efficiencies that could be leveraged or modernization benefits not being realized.

Flexible program that brings in migration experts to help reduce risk and accelerate customer projects, backed by experienced Snowflake Services Delivery consultants.

• Targeted Outcomes
• Proven practices and mechanisms for faster time-to-value
• Accelerated Snowflake impact, through roadmap prioritizing the right things during the migration journey
• Instill organizational change with end-to-end support through across every phase — assessment through post-migration

Series of interactive workshops with Snowflake business, industry, technical experts to provide your team direction, recommendations and a plan of action to map out the “how”, tie use cases to go-lives and have a plan for healthy adoption.

• Example Outcomes
• Clear alignment on target business outcomes, value propositions, and strategic AI/ML priorities
• A well-defined and shared understanding of the current state and desired future state
• A comprehensive plan detailing stakeholder roles, prioritized use cases, roadmap themes, and quick wins to jump-start execution