Insights at the Pace of New Trends: AI-Driven Conversational Analytics for a Global Fashion Brand
Case Study Details:
Industry
Fashion
Region
USA
Technology
Snowflake Cortex Analyst · Model Context Protocol (MCP) · LLMs · FastMCP · Azure Container Apps · OAuth
The Challenge
The client, a global retail and fashion brand, runs on data—Sales, Inventory, Marketing. But as the business grew, every question turned into a ticket. Users waited on analysts to write SQL and build reports, and by the time answers arrived, the moment had often passed.
- Every question became a ticket. Decisions waited on analysts.
- Insights lived behind SQL. Non-technical users couldn’t ask.
- Data sat in silos. Cross-domain answers meant stitching tools together.
- Governance couldn’t bend. Access controls had to hold, no matter what.
Traditional dashboards had reached their limit—the client needed a faster, simpler way to ask questions and get trusted answers without compromising enterprise controls.
Evoke's Approach
Evoke Technologies built a conversational analytics platform that lets business users talk to enterprise data in plain English — secure, governed, and grounded in the company’s own definitions. At its core, a custom MCP server connects ChatGPT to Snowflake, keeping the experience simple while the controls stay firmly in place.
- Ask in plain English. “Top-selling products last quarter?” returns a clear, structured answer in seconds.
- Routes each question to the right domain. A language-model layer sends each question to Sales, Inventory, or Marketing automatically.
- Turns questions into trusted SQL. Snowflake Cortex Analyst translates questions into optimized SQL using the client’s own semantic models.
- Secure by design. The MCP server, built on FastMCP in Python, brokers every call. OAuth handles authentication: access controls scope each user to what they’re allowed to see.
- Built to scale. Containerized with Docker and running on Azure Container Apps, ready to extend to new domains.
The Outcomes
The platform shifted enterprise analytics from a request-and-wait model to a self-service, on-demand capability—measurably changing how the business operates.
| Dimension | Before AI-Driven Analytics | After AI-Driven Analytics |
|---|---|---|
| Time to insight | Hours to days, gated by analyst availability | Seconds, on demand, directly to the business user |
| Who can ask questions of the data | SQL-literate analysts only | Any business user in Sales, Inventory, or Marketing |
| Workload on data and analytics teams | Saturated with ad-hoc reporting requests | Freed to focus on modeling, forecasting, and platform work |
| Cross-domain analysis | Manual stitching across multiple tools and teams | Unified conversational access across Sales, Inventory & Marketing |
| Security & governance | Manually enforced through restricted access to reports | Built into every query via MCP, OAuth & semantic models |
| Scalability | Linear—each new question added effort | Elastic, cloud-native foundation ready to expand across domains |
Strategic Value Delivered
Evoke turned enterprise data into a real-time decision asset through a conversational analytics layer — secure, governed, and trusted by design. The result is a new operating model in which every business user is an analyst, and every decision is grounded in the freshest available data.
