Unleashing Intelligent Automation: Exploring the Power of Cortex AI

Snowflake Cortex AI will redefine how organizations harness the power of data and intelligence. Our Snowflake leaders have shared a vision of a technological shift as transformative as the rise of cloud data platforms themselves. Iʼm energized by this vision because Iʼve seen firsthand how Cortex AI is already enabling enterprises to unlock insights faster, operationalize AI at scale, and bring intelligence directly to where their data lives.

With Cortex AI, insurers are accelerating fraud detection with real-time claims intelligence, healthcare providers are transforming patient outcomes through faster insights, and financial institutions are reimagining risk management with AI-driven decisioning—all seamlessly powered by the Snowflake Data Cloud.

To move from early innovation to widespread adoption, organizations need a practical and secure way to integrate AI directly into their data workflows. At Snowflake, weʼre committed to being the best place to build, deploy, and scale AI applications—empowering organizations to harness AI where their data already resides, without complexity or compromise.

Weʼre focused on making Cortex AI accessible to every business by combining rapid innovation with the trusted foundations of governance, scalability, and security that Snowflake is known for. Our approach enables companies to build on proven data strategies while embracing the new frontier of AI-powered applications—creating systems that adapt as models evolve, capabilities expand, and business use cases grow.

Today, Iʼm excited to share how weʼre bringing this vision to life with new Cortex AI capabilities designed to help you move beyond experiments into production-ready AI systems that can be trusted with your most critical business decisions.

Principle 1: Bring AI to the Data, Not Data to the AI

One of the biggest hidden costs in enterprise AI is data movement. When companies use external AI platforms, they often need to extract, duplicate, and ship sensitive data outside of their governed data environments. This creates new risks, high latency, and spiraling costs.

Snowflake Cortex AI flips this model: it brings AI directly to the data within Snowflakeʼs secure Data Cloud. Instead of exporting millions of rows of claims, transactions, or medical records into external APIs, enterprises can run advanced AI workloads where the data already resides.

• Insurance Example: Instead of moving historical claims data to an external AI provider, insurers can train and deploy fraud detection models directly in Cortex. This reduces risk of data exfiltration while enabling real-time fraud scoring.
• Healthcare Example: Patient summaries can be generated inside Snowflake, avoiding compliance nightmares of moving PHI to external servers.
• Banking Example: KYC document verification and summarization can be powered by AI directly on in-house customer data, without exposing it to external APIs.

Why not OpenAI? While OpenAI is powerful for model innovation, it requires sending sensitive data outside your enterprise perimeter—raising data

sovereignty and compliance concerns. Cortex removes that barrier by keeping AI fully inside your Snowflake environment.

Principle 2: Trust Through Built-in Governance and Security

In regulated industries, trust and compliance are not optional—they are existential. Moving data outside of governed environments risks violations of HIPAA, GDPR, SOC2, or financial regulations.

Cortex AI is natively integrated with Snowflakeʼs governance features: role- based access, dynamic data masking, column-level security, row access policies, and full auditing. This means every AI interaction respects the same governance model that protects your data today.

• Insurance Example: Fraud investigation teams can safely access enriched fraud scores while still adhering to masking policies that restrict Personally Identifiable Information (PII).
• Healthcare Example: Doctors see AI-summarized medical histories without exposing sensitive patient identifiers.
• Banking Example: Compliance officers can review AI-flagged suspicious transactions with audit trails for every query.

Why not Azure OpenAI or Bedrock? While cloud providers claim security, they often require duplication into their own AI environments. Snowflake Cortex AI is different—your governance stays intact because the data never leaves Snowflake.

Principle 3: Scale Intelligence Without Complexity

Enterprise AI must scale from small experiments to production-grade deployments. Most organizations struggle here: experiments on external platforms donʼt scale because they need complex pipelines, connectors, and ETL jobs.

Cortex AI is serverless by design. You donʼt provision GPUs, clusters, or manage scaling—itʼs handled automatically. From a SQL query, a Python Snowpark function, or a Streamlit app, AI becomes available instantly at scale.

• Insurance Example: A small pilot model for auto-claims fraud can seamlessly scale to thousands of claims scored per hour without infrastructure redesign.
• Retail Example: Customer feedback summarization can start as a test and grow into a fully automated NPS analytics engine.

Why not Google Vertex AI? While powerful, Vertex requires building pipelines and ML Ops complexity. Cortex cuts out that overhead by embedding scale directly into Snowflakeʼs elastic compute model.

Principle 4: Unify Structured, Semi-structured, and Unstructured Data for AI

Most enterprises deal with heterogeneous data: transactions (structured), logs and JSON (semi-structured), PDFs and images (unstructured). External AI platforms    require stitching this together with  painful  preprocessing  and connectors.

Snowflake Cortex AI leverages the unified data architecture of Snowflake: structured, semi-structured, and unstructured data live side by side and are queryable with a single engine. Cortex can run embeddings, retrieval- augmented generation (RAG), or ML training across all data types.

• Insurance Example: Combine structured claim fields with unstructured adjuster notes to detect fraud more accurately.
• Healthcare Example: Use lab results (structured) with physician notes (unstructured) for predictive diagnosis.
• Banking Example: Enrich structured KYC profiles with scanned document summaries for faster onboarding.

Why not OpenAI? Itʼs model-first, not data-first. Without unified enterprise data handling, enterprises are forced to spend millions on connectors, ETL, and pipelines. Cortex removes that cost and complexity.

Principle 5: Stay Open and Interoperable with the AI Ecosystem

Snowflake knows enterprises wonʼt bet on a single model or vendor. Thatʼs why Cortex AI embraces openness: it can orchestrate proprietary LLMs, open- source models, or even external APIs while keeping data inside Snowflake.

• Insurance Example: Use open-source fraud models combined with Cortex embeddings for hybrid detection pipelines.
• Healthcare Example: Combine Cortex summarization with external medical ontologies.
• Banking Example: Blend third-party AML models with in-house AI workflows—all governed by Snowflake.

Why not OpenAI or Azure OpenAI? They primarily push their proprietary

models, locking you in. Cortex is model-agnostic and lets you evolve with the ecosystem.

Principle 6: Accelerate from Experimentation to Production with Serverless AI

Many AI projects die in the “proof of concept trapˮ. They work in a lab but never make it to production due to infra, DevOps, and integration overhead.

Cortex AI is designed for frictionless production. A SQL analyst can use an LLM function today, a data scientist can wrap it in Snowpark tomorrow, and a business team can consume it in Streamlit the next day—no migration required.

• Insurance Example: Claims fraud prototype can become a production fraud detection app directly in Streamlit on Snowflake.
• Healthcare Example: A medical summarization experiment can be embedded in hospital dashboards without new infrastructure.
• Banking Example: AML models can move from data scientist notebooks to production monitoring within the same Snowflake environment.

Why not Bedrock? It requires designing workflows separately for dev and production. Cortex collapses that gap—experiments and production live on the same platform.

Principle 7: Adapt as Models and Use Cases Evolve

AI is moving at breakneck speed. Models that were cutting edge last year may feel obsolete today. Locking into a single vendorʼs roadmap is risky.

Cortex AIʼs openness ensures enterprises can swap, upgrade, or augment models without rebuilding pipelines. As better open-source or commercial models emerge, you can plug them in while keeping governance and data architecture intact.

• Insurance Example: Switch from GPT-3.5 to Llama 3 for claim notes summarization without redesign.
• Healthcare Example: Replace a summarization model with a domain- specific clinical LLM as they become available.
• Banking Example: Add fraud detection models specialized for crypto transactions without starting over.

Why not OpenAI? Vendor lock-in. Youʼre bound by their models, pricing, and roadmap. Cortex ensures freedom of choice.

Principle 8: Empower Every Builder—From SQL Analysts to Data Scientists

AI should not be the privilege of a few PhDs. With Cortex AI, every builder in your company can contribute—SQL analysts, BI developers, data scientists, and application engineers.

Snowflake Cortex AI provides native functions for SQL users, Snowpark APIs for Python developers, and Streamlit integration for app builders. This democratizes AI, accelerating adoption across the enterprise.

• Insurance Example: SQL analysts query fraud scores, data scientists refine models, and app developers build adjuster tools—all on the same Cortex fabric.
• Healthcare Example: Analysts run cohort analyses, doctors see AI-driven patient summaries, IT teams build clinical apps.
• Banking Example: Risk analysts query suspicious transactions, ML teams enhance models, and compliance officers consume results—all seamlessly.

Why not Azure OpenAI or Vertex? They often limit usability to data scientists and engineers. Cortex is built for the entire enterprise workforce, not just specialists.

So, Why Choose Cortex AI Over Others?

• OpenAI is model-first, not enterprise-first—powerful models, but risky for compliance and data sovereignty.
• AWS Bedrock and Azure OpenAI are cloud-first, not data-first—strong ecosystems, but require moving or duplicating enterprise data.
• Google Vertex AI is ML-first, not business-first—flexible, but too complex for cross-enterprise adoption.

Snowflake Cortex AI is different:

• Data-first (AI comes to the governed enterprise data).
• Governance-native (no compliance compromises).
• Scalable and serverless (no infra burden).
• Open and future-proof (no vendor lock-in).
• Built for everyone in the enterprise (not just data scientists).

If your enterprise wants AI that scales securely, works with all your data, and empowers every builder while keeping you compliant—Cortex AI is the answer.

Snowflake Cortex AI: Key Functions & Capabilities

Snowflake Cortex AI offers a rich suite of tools embedded directly in the Data Cloud. These tools span LLM functions, document processing, intelligent search & agents, and observability—each designed to tackle enterprise AI use cases securely and at scale.

1. Cortex LLM Functions (AISQL)

These built-in AI functions let you generate text, extract insights, analyze sentiment, translate languages, and embed vector representations—all through familiar SQL or Python:

• COMPLETE ( SNOWFLAKE.CORTEX.COMPLETE(model, prompt_or_history, [options]) )
  A general-purpose LLM interface supporting text generation, code, reasoning, and even image-based tasks like captioning or chart interpretation. You choose from top-tier models such as claude-3-7-sonnet, mistral-large2, or Snowflake-optimized versions like snowflake-llama-3-1-405b for cost-optimized inference.

• EXTRACT_ANSWER: Retrieves a direct answer to a query from unstructured text.

• SUMMARIZE: Produces concise summaries of longer text passages.

• SENTIMENT: Scores text with sentiment between –1 (negative) to +1 (positive).

• TRANSLATE: Performs multilingual translation with specified source/target languages.

• EMBED_TEXT_768: Generates numerical embeddings (768-dimension vectors) for semantic search and RAG workflows.

Helper Functions:

• COUNT_TOKENS: Checks token usage to avoid exceeding model limits.

• TRY_COMPLETE: Similar to COMPLETE but returns on failure.

Cortex Guard: Optional layer to filter harmful or unsafe content using Metaʼs Llama Guard 3.

2. Cortex Search

A managed Retrieval-Augmented Generation (RAG) service combining vector and keyword search with built-in ranking—ideal for building search experiences or chatbots directly on your Snowflake data:

• Use to index text columns, enabling low-latency semantic and lexical search without managing embedding infrastructure.

• Automatically refreshes indexes based on data changes, and integrates with LLM functions for grounded AI.

3. Cortex Analyst

A fully managed natural language interface that transforms plain-language questions into SQL:

• Interprets user queries and generates accurate SQL, even with follow-up context.

• Offers REST API access, custom instructions, and stored query suggestions.

• Enables BI-style, self-service analytics without needing SQL mastery.

4. Cortex Agents

An orchestration layer allowing AI agents to reason, plan, and execute multi-step workflows:

• Leverages tools like Cortex Search and Analyst for hybrid querying across structured and unstructured data.

• Supports context-aware, multi-turn reasoning, dynamic tool selection, and integration with external AI models or services

5. Document AI

Ideal for extracting structured data from documents, tables, forms, and even handwritten content using the proprietary Arctic-TILT model:

• Supports zero-shot extraction or fine-tuning per use case (e.g., invoices, financial statements).

• Enables continuous document processing pipelines via streams and tasks.

6. Cortex Fine-Tuning

• Allows you to customize LLM behavior with your own data.

• Build fine-tuned models specific to your enterprise use cases.

• Models are private and managed within your Snowflake environment—no data shared across accounts.

7. Snowflake Copilot

An AI assistant embedded in the Snowflake UI:

• Helps generate and optimize SQL queries.

• Provides guidance on Snowflake features and workflows—empowering non-technical users.

8. AI Observability

Currently in preview, this provides insight into AI workloads:

• Offers telemetry, traceability, and performance metrics for AI use cases.

• Supports debugging, model evaluation, and assurance of compliance.

9. ML Functions

Beyond generative AI, Snowflake offers classic ML via SQL:

• Functions for forecasting, anomaly detection, classification, and contribution analysis.

• These bring predictive analytics closer to data teams without requiring custom model infrastructure.

How to Use Cortex AI in Snowflake: A Step-by-Step Guide

Snowflake Cortex AI is designed to make LLM + AI workloads enterprise- ready by keeping models and data in one governed platform. Unlike external AI services, Cortex ensures data security, low latency, and seamless integration with your Snowflake ecosystem.

Here are the end-to-end steps to start using Cortex AI:

Step 1: Prepare and Store Your Data in Snowflake

• Upload your enterprise data (structured, semi-structured, or unstructured) into Snowflake tables.
• Cortex AI works directly on this governed data, so thereʼs no need to move it outside.
• For unstructured data (documents, claims, patient records, contracts), Cortex can automatically chunk, embed, and vectorize it.

Example: An insurance company loads historical claims into Snowflake to detect fraudulent activity.

Step 2: Enable Cortex AI in Your Account

• Cortex is an account-level feature provided by Snowflake.
• Once enabled, you gain access to pre-built functions for text generation, embeddings, summarization, translation, and more.
• No infrastructure management or external API integration is needed —

Cortex runs natively in Snowflakeʼs compute layer.
Example: A healthcare provider enables Cortex to analyze clinical notes without needing a separate ML infrastructure.

Step 3: Explore Pre-Built Cortex Functions

Cortex provides serverless AI functions that can be called directly inside SQL or Snowpark. The main categories are:

• Text generation → chatbots, customer response automation.
• Embeddings → semantic search, recommendation engines.     
• Summarization → document or patient record summaries.
• Translation → multilingual document handling.
• Sentiment Analysis → customer feedback or fraud claim assessment.

Example: A bank uses sentiment analysis on call-center transcripts to detect unhappy customers.

Step 4: Create Vector Stores for RAG (Retrieval Augmented Generation)

• Cortex lets you create vector stores directly from Snowflake tables.
• You can chunk documents, embed them, and store embeddings in a Snowflake table.
• These vector stores power RAG workflows, where large language models retrieve relevant context before generating responses.

Example: A law firm builds a vector store of contracts, enabling lawyers to query legal clauses conversationally.

Step 5: Build Applications (RAG, Chatbots, Predictions)

Once vector stores are ready, you can build end-to-end AI applications like:

• Enterprise chatbots with domain-specific knowledge.  Fraud detection with natural-language reasoning.
• Medical record summarization for physicians.
• You can integrate these with Streamlit, custom dashboards, or APIs.

Example: A retail company builds a chatbot that answers product-related questions using Cortex vector stores and embeddings.

Step 6: Govern and Monitor AI Workloads

Since Cortex runs inside Snowflake, it automatically inherits Snowflakeʼs governance features:

• Role-based access control (RBAC)  Data masking and tokenization Audit trails
• You also get visibility into query history, cost, and performance for all Cortex workloads.

Example: A bank ensures that only compliance officers can access AI-powered KYC workflows, while Cortex enforces masking of sensitive customer data.

Step 7: Scale Seamlessly Across Use Cases

• Cortex is serverless, so it scales automatically based on workload — no need to size or manage warehouses for AI tasks.
• This makes it easy to experiment with prototypes and then roll them out at enterprise scale without re-architecting.

Example: A global insurance firm starts with a small fraud detection POC and scales it to thousands of claims daily with zero infrastructure changes.

Why Cortex Is Different From External AI APIs (e.g., OpenAI, Anthropic, etc.)

While OpenAI, Anthropic, and other providers focus on general-purpose LLMs, Cortex is designed for                    enterprise   +   governed   data   use   cases. Key differentiators include:

• Data stays in Snowflake → no data exposure risks to external APIs.
• Governed AI → inherits Snowflakeʼs security, compliance, and access policies.
• Integrated vector stores → no need to manage external vector databases (like Pinecone or FAISS).
• Serverless simplicity → no tuning, scaling, or infra overhead.
• Unified platform * SQL analysts, data engineers, and ML teams can all build AI without leaving Snowflake.

In short: If your data lives in Snowflake, Cortex is the most secure, cost- effective, and enterprise-ready way to operationalize AI.

In todayʼs AI-driven world, the ability to securely leverage large language models (LLMs) and advanced machine learning capabilities directly within the data platform is not just an advantage—it is a necessity. Snowflake Cortex embodies this principle by bringing the power of AI natively to where the data lives. By uniting governance, scalability, real-time intelligence, and compliance under one roof, Cortex removes the barriers that organizations typically face when adopting generative AI and LLM solutions.

Unlike external AI providers, which often require complex integrations, risky data movement, and fragmented governance, Cortex ensures that enterprises can innovate faster while keeping their data private, compliant, and performance-optimized. Whether itʼs detecting fraudulent claims in insurance, automating KYC in banking, or summarizing patient histories in healthcare, Cortex empowers organizations to turn their data into real-time, actionable intelligence with trust at its core.

Choosing Cortex isnʼt just about adopting AI—itʼs about embracing a future where AI and data are seamlessly integrated, delivering measurable business value without compromising security or efficiency. For enterprises looking to stay ahead in the competitive landscape, Cortex represents not only the smarter choice but also the most responsible path forward.