Ride the AI wave in QA.
AI agents that live in your CI/CD. They generate tests, fix flakiness, and triage failures — while your engineers ship features. Your code stays yours.
- Runs in your CI
- Code stays in your repo
- EU-hosted · Frankfurt
- Zero data retention
qawave · sdlc-pipeline.log
live- [req-risk]DiscoverySTORY-2841 — DoR 0.82 · 1 ambiguity flagged (SSO timeout)
- [dor]RefinementStory passes DoR gate · risk: medium · regulatory: PCI scope
- [test-architect]DesignCoverage plan: 18 cases · NFR budget p95 < 200ms · trace matrix linked
- [code-qa]DevelopmentMR !4521 review — unit gap on session.refresh()
- → 8 Playwright + 2 contract tests proposed (MR !4522)
- awaiting human review →
- [execution-triage]CI / Dev GateSuite green · flake rate 1.8% (was 4.2%)
- login.spec.ts:203 — timing race · patch: waitForState('idle')
- [security-nfr]PreprodSAST clean · DAST 0 high · perf p95 +12% over budget
- release risk: needs SRE waiver
- [prod-sentinel]ProdEscaped defect: checkout retry loop · test gap → backlog
- [quality-intel]SDLCDoR 89% · DCE 92% · MTTR 41m · gate pass 76%
Live SDLC pipeline — agents propose, humans approve, your repo stays yours.
Agent infrastructure: Anthropic · Vercel · Supabase · Cloudflare
Your test suite is costing you more than you think.
12 h
/ engineer / week
lost to flaky tests
DORA State of DevOps 2024. 100-engineer team = ~$500k/yr in lost capacity (assuming $120k/eng/yr loaded cost).
30–40 %
of CI budget
gone before a single line of new code ships
Forrester Wave: Continuous Automation Testing, Q4 2025. Teams budget for tooling; the real cost is in reruns.
3–6 mo
hiring cycle
for a Senior SDET
Internal estimate based on public job board data; mileage varies by market. When you finally hire, they spend ~50% of their time on framework maintenance rather than new coverage.
If any of this sounds familiar, let's talk.
AI agents that actually ship tests.
Not record-replay. Not screenshots. Real, maintainable test code — generated, healed, and orchestrated by AI, approved by your engineers.
Generate
From user stories, production traffic, or PR diffs, our agents author Playwright / Cypress / API tests that survive refactors and pass your review.
Heal
Selector drift, timing races, data churn — agents detect root cause at the intent level, not the selector level, and open a fix PR within minutes. Your SDETs stop babysitting, start strategizing.
Triage
When CI fails, the Bug Triager agent analyzes logs, traces, and diffs to propose root cause and assignee — in seconds, not hours. Incident response starts before the pager rings.
We don't just build agents. We run on them.
One founder. Forty agents.
QAWave is operated by one human (me, Tomas) and 40 AI agents. Sales outreach, marketing, customer support, code review, test delivery — all powered by the same agent architecture we sell to customers.
This isn't marketing. It's the architecture.
The same eval harnesses, the same agent design patterns, the same human-in-the-loop checkpoints — applied to your CI pipeline instead of our business.
Things we get asked before the call.
Still rerunning CI until it passes?
Short call. No pitch deck. We'll tell you honestly if it's a fit — or who to talk to instead.
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