Independent SaaS · Market intelligence
An AI-native market research SaaS that turns institutional flow and market structure into a clear, personalized daily read.
Product & Engineering
From idea to paid product
I owned product strategy, architecture and delivery across market-data pipelines, analytics, the web experience and subscriptions. Took the product from concept to production in three months.
The math comes first
Python computes and scores market signals before the model sees them. Options flow, dark-pool activity, open interest, volatility and dealer positioning feed a deterministic evidence layer; AI explains that evidence in plain English.
Trustworthy data, qualified conclusions
The engineering work includes provenance, freshness checks and fail-closed quality gates. Regression tests cover stale data, mismatched contracts and incomplete evidence so unsupported claims do not quietly reach the user.
An end-to-end SaaS system
Next.js on Vercel serves the product; Supabase provides the data and authentication layer; Railway runs Python pipelines. Massive supplies market data, Anthropic powers the explanation layer, Stripe manages subscriptions, and Resend delivers the briefs.
Making complex analysis usable
A personalized evening brief connects to supporting dashboards, institutional price levels, sector rotation and position tracking. Progressive disclosure keeps the first read concise while preserving the evidence underneath.
Product context
Built for research and education. Signal quality and evidence matter more than promises of trading returns.
