Spec interview · Implementation · Adversarial QA · The full pipeline
An AI engineer and QA agent that handle your entire development lifecycle.
Most tools give you one piece — code generation or code review or testing. Our managed framework starts before the code: an AI-assisted spec interview that builds complete requirements, then handles implementation, adversarial code review, external scenario testing against a live instance, and human-approved merge. Two agents, one pipeline, managed for you.
30-minute call. See the pipeline in action.
No spec. No QA. PRs piling up.
AI coding tools start writing code the moment you hit enter — no requirements gathering, no acceptance criteria, no edge case analysis. Then nobody checks the output before it reaches your reviewers.
Code starts before the spec does
AI tools start generating code from a one-line prompt. No requirements gathering. No edge case analysis. No acceptance criteria. You get code that "works" but doesn't do what you actually needed.
Tool sprawl, zero integration
One tool generates code. Another reviews it. A third runs tests. None of them share context, and you're the glue holding it together.
AI code with no QA gate
AI-generated code lands in PRs and your senior engineers are the only safety net. Nobody writes tests. Nobody checks against the spec.
PR review is the bottleneck
PRs sit for days waiting for review. Your best engineers spend more time reviewing than building. Velocity drops while the backlog grows.
From backlog to merge — one managed pipeline
The developer doesn't grade their own homework. Every line of code passes through adversarial review and external scenario testing before a human ever looks at it.
Spec Creation
Every feature starts with a conversation, not a cursor. The agent sits down with you and interviews you like a senior engineer would — one question at a time, probing edge cases, surfacing constraints you hadn't considered, cross-referencing failure patterns unique to your codebase. What comes out the other side is a complete spec: acceptance criteria, known risks, testing strategy, and clear boundaries on what's in scope and what isn't. By the time code starts, everyone — human and machine — agrees on what "done" looks like.
Spec Approved
You read the spec, tweak what needs tweaking, and approve it. The card moves to Ready in your project tracker. That's it — that's your last manual step before code starts flowing. Everything from here is automated.
Automated Pickup & Implementation
A scheduled job detects the ready card and spins up the engineer agent. It reads the approved spec, writes the implementation following your conventions and architecture, adds test coverage, and opens a clean pull request — all without anyone asking it to. You wake up to a PR, not a to-do list.
Adversarial Code Review
A separate QA agent tears the PR apart. It reviews against the original spec, then cross-checks a library of failure patterns built from every past human code review on your project — real bugs, real "request changes" comments, real mistakes your engineer agent has made before. These aren't generic lint rules. They're your team's institutional knowledge, encoded and enforced automatically.
External Scenario Testing
Your app gets spun up in a fresh, isolated environment. Gherkin scenarios execute via Playwright against the running instance — real clicks, real forms, real user flows. Here's the key: the implementer agent and the tester agent have zero access to each other's code. They run in completely separate contexts. It is physically impossible to game the test. If the scenario fails, the code is wrong.
Agent Review Loop
When QA finds issues, it sends them back to the engineer. The engineer fixes. QA reviews again. The engineer fixes again. Back and forth, automatically, until every check passes and every scenario is green. No human needs to babysit this — the agents negotiate the fix between themselves.
Human Review
By the time your team opens the PR, it's already survived adversarial code review and end-to-end scenario testing. You're reviewing clean, tested, spec-compliant code — not debugging someone else's first draft. And if you do request changes? The entire cycle restarts: QA review, scenario testing, agent loop. Same rigor, every iteration, until you're satisfied and hit merge.
Continuous Self-Improvement
After every merge, a nightly process mines the PR for new failure patterns — what the QA agent missed, what the human reviewer caught, what broke in ways nobody expected. Those patterns get folded back into the QA agent's review criteria for the next PR. The system gets smarter with every cycle. Over time, fewer mistakes make it to human review, and you can go longer between code reads without anything slipping through.
The Engineer Agent
A senior-level AI software engineer that interviews you on requirements, builds a complete spec, then writes code with tests, follows your conventions, and opens clean PRs — working alongside your team, not replacing them.
AI-assisted spec interview
Before writing a line of code, the agent interviews you — asking clarifying questions one at a time, checking edge cases, referencing known failure patterns from your codebase. The output is a complete spec with acceptance criteria, constraints, and testing notes. No other AI coding tool does this.
Autonomous task pickup
Pulls from GitHub Issues, Linear, Jira, or Asana. Works from the full spec — not just a ticket title and a prayer.
Tests are part of every change
Every feature includes test coverage. Every bug fix includes a regression test. Tests run before code ships.
Follows your conventions
Reads your codebase, learns your patterns, follows your linting rules and architecture. Doesn't impose its own style.
Small, focused PRs
One concern per PR. Clear titles, context-rich descriptions. Uses git worktrees so parallel tasks never conflict.
Reports back
Lets the team know when work is done, what decisions were made, and why. No black-box surprises.
Adversarial by design
Assumes code is wrong until verified. Doesn't say "great work." Finds problems or says nothing.
Reviews against the spec
Every PR is checked against the original issue requirements — not just code style or linting rules.
Knows your failure patterns
Maintains a library of past bugs specific to your codebase. Catches the same class of bug once, prevents it forever.
Verifies before posting
Traces code paths, checks surrounding context, confirms the issue is real. Signal, not noise.
Inline PR comments
Actionable findings on the exact line, with severity and fix suggestions. Advisory — never blocks merges.
Learns from feedback
When a finding is dismissed, that becomes a pattern to avoid. When a real bug is caught, that becomes a pattern to watch.
The QA Agent
An adversarial AI QA agent that reviews every PR against the spec and known failure patterns — then runs your app through real user scenarios before a human ever looks at it.
info The QA agent is advisory — it posts findings and recommendations, but never blocks merges. Your team always makes the final call.
Your code gets tested against real user flows — not just linted
Most AI code review tools do static analysis. We spin up your actual application in Docker, run Gherkin scenarios via Playwright against it, and post a structured pass/fail table directly on the PR. Nobody else does this.
Docker spins up your app
A fresh Docker instance of your application is launched with the PR's changes applied. Not a mock — your real app, your real dependencies.
Gherkin scenarios define the test
Human-readable Gherkin scenarios describe expected user behavior: "Given a user is logged in, When they click checkout, Then the order is placed." Every scenario traces back to a requirement.
Playwright executes against the live instance
Playwright drives a real browser against your running application — clicking buttons, filling forms, navigating pages. Exactly what your users do.
Structured results on the PR
A pass/fail table lands directly on the PR as a comment. Every scenario, every assertion, every result — visible before a human reviewer opens the PR.
Most tools stop at static analysis. They scan the diff, check for patterns, and maybe run a linter. That catches surface issues — not the bugs your users will find. External scenario testing catches what static analysis can't: broken user flows, integration failures, and regressions that only show up when the app is actually running.
Whether you have a team or need one
CTOs & Engineering Leads
You have a dev team. You want to augment capacity without the headcount — and without sacrificing code quality.
- — PR reviews that take days, not minutes
- — Junior devs shipping without test coverage
- — AI-generated code with no QA process
- — Velocity lost to review bottlenecks
Add a senior engineer and dedicated QA reviewer — without the headcount.
Non-Technical Founders
You need dev capacity you don't have — and you're tired of outsourced work coming back wrong with no QA process.
- — Contractor work that doesn't match the spec
- — No tests, no reviews, flying blind
- — Bugs discovered by your users, not your process
- — No visibility into what's being built or why
A development team that follows best practices by default.
Works With Your Stack
Commits, PRs, and code reviews happen on GitHub. Task tracking works with whatever you already use — Linear, Jira, Asana, GitHub Issues, or any tool your team prefers. CI runs wherever you run it.
update More VCS platforms coming soon. GitHub is where commits and reviews live today.
Pull Requests
Full PR lifecycle with inline review comments
Any Task Tracker
Linear, Jira, Asana, GitHub Issues — your choice
Any CI
GitHub Actions, CircleCI, or your existing pipeline
Your Repos
Works with your existing repository structure
See the full pipeline in action
Request a demo. We'll walk you through the framework with your actual codebase.
Got it — we'll be in touch
We'll follow up within one business day with a tailored assessment. Changed your mind?