Custom BI Platform

The problem

A mid-sized professional services firm had spent two years adapting Power BI and Tableau to its business without success. The metrics that ran the firm reflected processes no vendor tool understood. Every customisation came out brittle. The important requirements (real-time what-if modelling, metrics embedded in operational workflows) had been abandoned as impossible.

Managers were filling the gap by hand, compiling reports from exports every week.

The build

A custom BI platform on a code-first principle: every metric is code, version-controlled, type-checked, and reviewed like any other software. Metrics as code, type safety, composability and observability anchored the architecture. Metrics as code, so definitions are explicit and auditable. Type safety end to end, so a metric cannot silently change meaning. Composability, so new metrics build from existing ones. Observability by design, so the user can trace where a suspect number came from.

The feature that earned the most trust was data quality validation built into the calculation engine. A figure that fails validation never reaches a dashboard, so decision-makers stopped having to ask whether they could believe the screen.

The workflow

FastAPI serves the calculation engine, Next.js drives the front end, PostgreSQL holds the data, and Redis caches the expensive aggregations. Docker Compose keeps deployment boring. What-if scenarios run live against the real calculation engine, so the answers hold up under scrutiny.

The outcome

Managers got 5-10 hours a week back from report compilation. New metrics ship in days, on the firm's own schedule. The three-year cost projection was 40-60% below the equivalent vendor stack, and that settled the build-vs-buy argument inside the firm for good.