Case study of my own product work. It describes what was built and tested; no customer, revenue or usage figures are claimed.

The problem

Brand and partnership teams decide where to compete, whom to partner with and which sponsorships to back, often from scattered decks and gut feel. The goal for BrandGrowth OS was a single workspace that turns those questions into a recommended action plan, without pretending to know numbers it has not measured.

What was built

Four engines and one plan. Visibility Radar, BrandMatch, Sponsorship Intelligence and Partnership ROI each score one area, and a central Opportunity Engine combines them into a recommended action plan.

Evidence first. The scoring engine is a separate, versioned, rule-based package. When data is missing, it says "not measured" instead of inventing a value, so every score can be explained.

API-first application. The product is a Next.js and TypeScript application with a versioned API, backed by PostgreSQL. The database listens only on the server itself.

Accounts done properly. Sign-up with email verification, two-factor authentication with recovery codes, sign-in through common identity providers, and a weekly digest email.

Self-hosted delivery. The app runs as a system service behind the main web server, path-hosted under an existing site, so TLS and access rules stay in one place.

How quality was controlled

Where it stands

BrandGrowth OS runs as a private demo while release candidates go through qualification. A public launch is deliberately held back until the gates and owner review are complete.

FAQ

Why rule-based scoring? Every score stays explainable and testable.

Can it run on a small server? Yes; it is self-hosted alongside other sites.

I take on a small number of advisory and hands-on systems engagements each quarter. Connect at virender.in.