Conversational Analytics

Let non-technical users query their data using natural language. The data was constrained, the business domain was well-defined, and the goal was an MVP with iterative improvements, not a full analytics platform.

A client's operational team needed to answer business questions from their data warehouse without writing SQL or waiting for a report from the analytics team. The data was well-structured, and the business domain was clearly defined, making it a strong candidate for a natural language interface.

Challenge

The technical challenge was not building a query engine from scratch. It was teaching an AI system what the data actually means in a specific business context. Column names, metric definitions, and business rules needed to be captured in a semantic layer that translates natural language questions into accurate database queries. Without that layer, the system would return technically correct but business-wrong answers.

What We Built

We used a managed analytics platform that acts as an orchestrator between a front-end application, a semantic layer, and a Snowflake data warehouse. The semantic layer holds business context, data mappings, metric definitions, and query templates. Pre-built front-end templates minimized UI development.

About 20% of the 100 to 120 hours of effort went into platform setup and integration. The other 80% went into semantic layer configuration: business context definition, data mapping, query tuning, and template customization.

What Changed

Operational team members can now get answers to business questions directly, without SQL and without waiting for the analytics team. When a managed platform fits the use case, it dramatically reduces effort. The work shifts from building infrastructure to configuring the semantic layer, which is where the real business value lives.