Lumina — AI-Powered Business Intelligence Platform

Lumina is an AI-native business intelligence platform that lets teams ask questions in plain language, get instant answers from connected data sources, and receive proactive alerts and trend summaries. It combines semantic search over structured and unstructured data with a conversational interface and a flexible report builder.

Product and operations leaders were drowning in dashboards and one-off SQL requests. Data lived in data warehouses, CRMs, support tools, and spreadsheets. Answering a single business question could take days and require engineering or analytics handoffs. Decision-making was delayed and insights were often stale by the time they reached stakeholders.

A single place where any stakeholder can ask 'What's driving churn this quarter?' or 'Which segments are most at risk?' and get accurate, sourced answers in seconds—without writing queries or waiting on analysts. The product should feel like a capable colleague that knows the business and can explain its reasoning.

We ran discovery with the client’s core product and ops leads to map how they ask questions and use data today. We prioritized a single, chat-first entry point with a sidebar for saved questions and reports. Simple answers first, with optional drill-down and explain controls. Desktop-first with a shared design system; mobile reserved for alerts and quick checks.

Next.js front end with a real-time WebSocket channel for long-running queries. Python backend services for query parsing, planning, and execution; we use a query planner that compiles natural language to optimized SQL via an internal DSL. Vector store (pgvector) for semantic indexing of schemas and docs. Caching layer (Redis) for repeated queries and precomputed aggregates. Event-driven pipeline for syncing metadata and sample data from source systems.

The client’s team reports noticeably faster answers to ad-hoc questions without writing SQL or waiting on analysts. The product is live internally and the client is using it to validate the approach before a wider rollout.