SaaS2025

OpenScope

A production SaaS that turns the noise of GitHub issues into AI-powered engineering insight.

Sole engineer — design, build, ship

Next.js
TypeScript
Supabase
Claude API

the problem

Maintainers and teams drown in issues. Across even a handful of active repositories, the signal — what's actually breaking, what keeps recurring, what to prioritise — is buried under hundreds of tickets, and there's no view that spans repos at once.

The goal was a product that ingests issues at scale and answers the questions a human would otherwise spend hours pattern-matching for, on demand and without babysitting a pipeline.

approach

A full-stack SaaS, owned end to end

I built OpenScope as a production Next.js application with full authentication, protected routes, and a set of scalable backend APIs — the whole lifecycle from schema to deploy as sole engineer.

Ingestion pipeline

The analysis engine is seeded from 100+ repositories and 500+ issues, scraped and indexed for on-demand analysis. A normalized ingestion pipeline cleans and shapes that raw data into a consistent schema before it ever reaches the insight layer.

On-demand AI analysis

Rather than pre-computing everything, insights are generated on demand against the indexed corpus, so the AI layer works from normalized, query-ready data instead of raw API payloads — keeping analysis fast and the results consistent.

architecture

1
GitHub API
issues pulled across 100+ repositories
2
Ingestion workers
scrape, normalize, and index
3
Postgres (Supabase)
normalized issue store + auth
4
Insight layer
on-demand Claude analysis over the index
5
Next.js app
authenticated, protected-route UI

outcomes

100+

Repositories indexed

500+

Issues analyzed

Full

Auth + protected routes

Scalable

Backend APIs

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