Production-grade QA for AI-built apps.
uberqa is continuous QA for teams running AI-built software in production. Monthly automated audits, drift detection, and signed inspector reviews — so the code your agents merged at 2am still works when real customers hit it at 9.
You ship a lot. How do you know it's actually working?
AI-built code is in your production environment. Real users. Real revenue. Real load. Cursor and Claude Code merge PRs while you sleep, the agent shipped another feature you only half-reviewed, and the system has to keep working through all of it.
Is yesterday's auth change still working? Did the migration drop a column? Are the AI-generated handlers handling the edge cases? Will a single bad actor max out your OpenAI bill tonight? You can't possibly check all of it — and the agent that wrote it has already moved on to the next ticket.
uberqa is the production-QA layer you didn't have time to build. Independent. Methodical. Running every month it's live — catching what your agents missed before your customers do.
What we check.
// bucket 8 ("design / UX quality") is intentionally out of scope. Subjective. Dilutes rigor. Available later as an add-on if the market wants it.
Agent-led. Human-signed.
Hand over the live URL plus repo or platform access. Scope confirmed by email within an hour. First scan inside 24 hours.
The playbook runs every month against your live build. Functional probes. Security checks. Data inspection. Drift detection between cycles auto-fires a re-check on material change.
Verdict each cycle: PASS / FIX / HALT. Severity-graded findings. Inspector-signed on Watch+ and Verified. Plain-English summary you can forward without translation.
Each finding ships with a paste-ready prompt for the AI that built it — Cursor, Claude Code, Lovable, Bolt, v0. Or pull findings via the uberqa MCP server and let the agent auto-remediate. The loop closes.
Findings ship as paste-ready prompts for the agent that built it.
AppSec scanners just report. uberqa speaks AI-build natively. Every finding includes a remediation prompt formatted for the exact tool that wrote your code — Cursor, Claude Code, Lovable, Bolt, v0, Replit Agent.
On Watch+ and Verified, the same findings are exposed via the uberqa MCP server. Open Cursor, say "fix the open uberqa criticals", watch your agent close the loop. We re-scan on the next push.
The Supabase service-role key is currently embedded in the client bundle (see build/_app/.../index-7a2c.js:1148). Move it to a server-side env var (SUPABASE_SERVICE_ROLE_KEY), expose only the anon key to the client, and route privileged operations through a server handler under /api/_internal/. Verify no service-role usage remains in any client-imported file. Once changed, redeploy and tag the change as 'remediation:F-001' so uberqa picks it up on the next scan.
A report your CFO can read. Findings your engineer will respect.
Plain-English verdict on page 1. Severity-ranked findings underneath. Evidence and reproduction steps for each one. A remediation playbook in the back.
open_sample_report ↗You're running AI-built software in production without a QA org behind it. Cursor and Claude Code ship faster than you can review. uberqa is the production-QA layer you didn't have time to build.
see Watch+ pricing →Five agent-merged PRs by lunch. Daily Cursor diffs. uberqa runs the production playbook every month against your live build, with deploy-triggered re-checks — so you find out before your customers do.
see how Watch+ works →Deliver AI-built work with a signed uberqa report attached. The Verified badge is the difference between 'looks ok' and 'inspector-approved.'
see the badge →Run by people who've been shipping software since the dial-up era.
uberqa is operated by engineers with 20+ years building and inspecting production SaaS — through Rails, microservices, the cloud migration, the mobile pivot, the JS framework wars, and now the AI-build wave.
We've seen what production grade actually looks like. We've seen what fails at 3am. The playbook is what twenty years of incident review distilled into a checklist.
> grep -i "shipped" career.log | head 2005 perl/cgi → first paid production deploy 2009 rails monoliths · early ec2 · pre-S3 backups by hand 2014 microservices, kafka, the on-call rotation that taught us what to check 2019 zero-downtime migrations · k8s · 100M-row schema rewrites 2022 series-B audit lead · sox controls · vendor due diligence 2026 uberqa · the playbook the last twenty years should have shipped with