Written by Jonathan Feng, Solutions Architect at Contextual AI, and Cameron Wasilewsky, Technical Lead at Snowflake

Enterprise AI is at a crossroads. While the promise of AI-driven automation and intelligence is more apparent than ever, getting solutions into production remains a formidable challenge. AI deployments in enterprises require more than flashy demos; they demand high accuracy, security, audibility, and seamless integration with existing data ecosystems.

Traditional RAG implementations require stitching together disparate tools for extraction, embedding, retrieval, and response generation, along with the significant complexities of managing and scaling the system for thousands of users. Due to this complexity, the majority of these RAG deployments never reach production.

At Contextual AI, we are tackling this problem head-on by providing an end-to-end platform for building specialized RAG agents that address knowledge-intensive enterprise use cases. The platform tightly integrates and jointly optimizes our state-of-the-art models into a unified, highly efficient system that can be deployed instantly at low cost, providing higher accuracy and production-grade performance out of the box.

The Challenge: AI That Works in the Real World

As a Solutions Architect here at Contextual AI, I’ve seen firsthand the limitations of standard AI implementations in enterprises. Most companies struggle to move beyond experimental AI pilots because:

• Data Security & Compliance: Enterprises cannot afford to expose sensitive corporate data to third-party AI services.
• Infrastructure Complexity: Managing GPUs, data pipelines, and AI workflows significantly burden engineering teams.
• Limited Accuracy & Reliability: Existing AI implementations, especially RAG, often suffer from hallucination and poor retrieval precision — especially at enterprise scale.
• Cost and ROI: Low accuracy leads to poor adoption and low ROI, which makes the cost of scaling to production-size clusters difficult to justify.

We built the Contextual AI Platform to bridge this gap, ensuring AI solutions are accurate, reliable, secure, scalable, and production-ready from day one.

Why Snowflake? A Game-Changer for Enterprise AI

The Snowflake Native App Framework has transformed how Contextual AI operates for Snowflake customers, allowing enterprises to deploy AI securely and efficiently within their existing Snowflake environment. By leveraging Snowpark Container Services (SPCS), our platform runs directly within customer environments, ensuring low latency and high performance while maintaining data sovereignty.

One of the biggest barriers to AI adoption is long sales and security review cycles. With Contextual AI as a Snowflake Native App, enterprises can bypass these hurdles, benefiting from:

• Faster Procurement: Businesses can purchase our solution using their existing Snowflake credit commitments, avoiding lengthy legal approvals.
• Security & Compliance: Our app runs inside each customer’s secure Snowflake account, ensuring strict data governance.
• Seamless Scalability: Leveraging Snowflake’s compute and storage, Contextual AI scales effortlessly across enterprise workloads.

This streamlined integration enables enterprises to deploy AI solutions faster, without added engineering overhead or compliance concerns.

Interested in seeing Contextual AI in action? Explore our Snowflake Native App today!

How to Use Contextual AI as a Snowflake Native App

Using Contextual AI’s Snowflake Native App is fast and requires minimal setup. More platform details can be found on our documentation page.

1. Install — Activate the Contextual AI app from the Snowflake Marketplace directly in your Snowflake account.

2. Grant permissions — Provide access to compute pool resources.

3. Launch app — Navigate to your installed Contextual AI application. Your account will be automatically created with your first login. Other members of your Snowflake organization can also access the application directly.

4. Create a datastore and agent — Load your multimodal documents and data into a datastore and create an agent that can retrieve and reason over that datastore.

5. Leverage platform APIs — Embed agent development and deployment into your existing workflows with powerful APIs.

6. Manage costs — Suspend your compute pool to control costs.

GRANT OPERATE ON COMPUTE POOL "CONTEXTUAL_AI_PLATFORM_GPU_COMPUTE_POOL"
TO ROLE ACCOUNTADMIN;

• To suspend your application, run this Snowflake query in any worksheet:

CALL CONTEXTUAL_NATIVE_APP.APP_PUBLIC.SUSPEND_APP();

• When you are ready to resume your application, run this Snowflake query in any worksheet:

CALL CONTEXTUAL_NATIVE_APP.APP_PUBLIC.RESUME_APP();

Technical Architecture Overview

Contextual AI’s architecture is designed for efficiency, sclability, and security, fully leveraging Snowflake’s Native App Framework:

• Data Ingestion & Extraction: Users can easily upload multimodal documents and connect to structured data.
• Active Retrieval & Reasoning: RAG agents iteratively retrieve and reason over data to optimize responses to complex tasks.
• Query Processing: Queries are processed using Snowflake’s GPU-powered inference on Snowpark Container Services (SPCS).
• Security & Deployment: The entire workflow runs within a customer’s secure Snowflake account, ensuring data privacy and compliance.

Final Thoughts

Contextual AI is redefining Enterprise AI by making AI-powered insights more accurate, scalable, and secure. With our state-of-the-art platform and deep integration with Snowflake’s Native App Framework, we remove the complexities of AI adoption, ensuring enterprises can deploy and scale AI-driven solutions effortlessly. By running natively within Snowflake, businesses can unlock the full potential of their data while maintaining the highest standards of security and compliance.

Ready to finally move your Enterprise AI projects into production? Explore Contextual AI on Snowflake today!