How Narwal Helped a Global Manufacturer Turn AI-Powered Product Sentiment into Enterprise-Wide Decision Intelligence

Summary

A leading global manufacturer had already deployed a Snowflake-native AI product sentiment assistant to analyze product reviews and surface actionable insights, but access remained limited to technical teams. Product and business functions had no direct way to query sentiment data, leaving the bulk of the organization dependent on technical staff for even basic feedback analysis.

Narwal built a secure ChatGPT Enterprise integration that brought existing Snowflake sentiment insights into a conversational interface business teams already used, without compromising data governance, access controls, or security standards. The result was a governed access pattern that made product intelligence available enterprise-wide while keeping Snowflake as the system of record.

Customer Challenge

The sentiment assistant itself worked well, but its value was capped by who could reach it. Querying the data required technical familiarity with Snowflake, which meant product managers, marketing teams, and other business stakeholders had no practical way to act on the insights generated for them.

• Restricted Business Access: Sentiment insights were primarily accessible to technical teams, limiting adoption and utility across product and business functions.
• No Enterprise-Grade Access Pattern: Existing tools lacked a secure, governed integration model compatible with ChatGPT Enterprise and enterprise identity standards.
• Data Security Requirements: Passing Snowflake data through an external AI interface required a middleware layer that could enforce access controls and prevent data exposure.
• Usability Gap: Business teams needed a conversational interface they were already familiar with, rather than a purpose-built analytics tool requiring onboarding.

Narwal’s Solution

Narwal designed and implemented a secure enterprise access pattern connecting Snowflake product review data to ChatGPT Enterprise through a governed middleware architecture.

Custom GPT with GPT Actions

Narwal built a Custom GPT configured with GPT Actions to enable structured, context-aware querying of product sentiment data directly within the ChatGPT Enterprise interface, giving business users a familiar entry point into Snowflake-backed insights.

Secure Middleware Layer

An Azure Functions-based middleware handled all data retrieval from Snowflake, enforced access controls, and ensured retrieved context was securely passed for downstream GPT analysis without direct data exposure.

Azure Blob Storage Integration

Azure Blob Storage served as a controlled intermediary for managing retrieved review context, supporting a clean and auditable data flow between Snowflake and the GPT interface.

OAuth-Based Access Control

OAuth authentication enforced a controlled access flow, ensuring only authorized enterprise users could interact with the sentiment assistant.

Business Outcomes

• Secure Enterprise Access Pattern Delivered: Narwal designed and implemented a reusable, enterprise-grade access architecture connecting Snowflake to ChatGPT Enterprise, establishing a governed model for broader AI adoption.
• GPT Actions Integration Implemented: Product sentiment data from Snowflake was made queryable through a Custom GPT, enabling structured conversational access to review insights within a familiar interface.
• Broader Business Access Enabled: Business and product teams gained direct access to sentiment insights through ChatGPT Enterprise, reducing dependence on technical teams for feedback analysis.
• Improved Usability: By surfacing insights within an interface teams already used, the solution lowered the adoption barrier and improved day-to-day utility of existing sentiment capabilities.
• Production-Ready POC Delivered: The complete integration, including the Custom GPT, middleware, access controls, and data flow, was delivered as a validated proof of concept ready for enterprise rollout.

Why Narwal

Connecting a governed data platform to an external conversational AI interface without compromising security required more than an API call. It required a middleware layer purpose-built to enforce access controls, an authentication model enterprises could trust, and an integration pattern designed for reuse beyond a single use case. Narwal combined deep Snowflake AI expertise with enterprise integration engineering to deliver a solution business teams could adopt immediately and security teams could approve with confidence.

Ready to Bring Your Data Platform to the Tools Your Teams Already Use?

Don’t let valuable insights stay locked behind technical access. Partner with Narwal to build secure, governed AI integrations that extend your existing data investments into the interfaces your business teams use every day.