Case StudyAI DevTools SaaS

Code audits in minutes, not a week.

Tech Audit helps teams upload a codebase, run an AI review, and receive prioritized security, quality, and maintainability findings in real time.

Sector: AI · DevTools
Frontend: Next.js · React
Backend: Supabase · Vercel
AI: Gemini audit engine
Outcome: 7+ days → 4 min

Tech Audit — engineers reviewing an automated AI code audit — A cream-toned generated cover for a technical product: people reviewing code, findings, and remediation priorities together.

By the numbers

4 minAverage audit time

−99%Review turnaround

43%Critical issues remediated early

+62NPS from pilots

Manual audits were too slow for shipping teams.

Engineering teams can wait a week or more for manual audit feedback. By then, the code has changed, the context is colder, and the fixes are harder to prioritize.

Tech Audit needed to turn audit work into a SaaS workflow: upload, analyze, stream findings, rank severity, explain the fix, and make the report shareable.

A production SaaS wrapped around an AI code reviewer.

We built the product shell, analysis workflow, billing layer, database model, reporting UI, and real-time status system around Gemini-powered review.

Next.js, React, Tailwind, shadcn/ui, and Framer Motion for the customer-facing audit dashboard
TypeScript serverless functions on Vercel with Drizzle, Supabase, and PostgreSQL
Server-sent events for real-time progress from upload through final report
Stripe Checkout, customer portal, subscription gates, webhook handling, and PostHog product analytics

	Before	After Ego Eimi
Audit turnaround	Seven-plus days of manual review	About four minutes, a −99% drop
Acting on findings	Cold context by the time fixes start	43% of critical issues remediated early
Coverage	Issues that slip past a busy team	92% of pilots found issues they would have missed
Pilot reception	No reusable audit workflow	+62 NPS from pilots

Old-site developer work photo used for Tech Audit

Old-site media

Findings teams can act on.

The product surfaces issues by severity, explains the risk in plain language, and keeps the remediation conversation tied to the code that triggered it.

92% of pilots said AI found issues they would have missed

See it work