You write what a change must do. The factory builds it, tests it and shows it works. An engineer, yours or ours, approves every change.
AI coding assistants made each developer faster, but someone still interprets each requirement, places the code, tests and reviews it.
The factory produces the code, the tests and the first round of verification. Your engineers decide what to build and approve each change.
of organizations say AI-generated code has grown too large for their teams to test fully (Tricentis, June 2026).
For SaaS products and enterprise systems whose backlog grows faster than the team: product teams building features, and IT departments changing their internal systems.
Your product team writes a specification the way it already does: what the change must do, and the acceptance criteria and scenarios that check it.
The factory decides the implementation. It reads your architecture first, so the code goes where your team would put it.
Each acceptance criterion maps to a test that exercises real behavior, including the failure paths.
Where the change touches the interface, with screenshots you can inspect.
The factory opens a pull request with reasoning, diff, tests and proof it works (test results, screenshots), then stops until your team, or ours, approves it, test changes included.
The fastest way to pass a test is to make it check less. A check that did not write the tests blocks any weakened test.
The factory asks a precise question, and that task waits until someone answers it.
Our forward deployed engineers were senior engineers before AI tools. They direct AI daily and reject its mistakes.
A senior engineer of ours inside your team, who runs discovery, writes the specs with your people and answers for the result in production.
A senior lead, shared with other clients, who sets up the factory on your architecture and keeps it tuned.
Running on your repository and your CI, directed and reviewed by the forward deployed engineer.
Contracted month to month. The pod works in time zones aligned with your working day.
We study your architecture, conventions and CI, then adapt our existing factory to your layers, test stack and pipeline.
The first specs run with your team watching, and your engineers correct the factory before trusting it.
New types of spec extend the factory without rebuilding it.
Code and tests, in your repository: every change merged through your review process, with its functional and visual tests running in your CI.
Records anyone can audit without asking us: which requirement produced which file, which test covers it, and what each run verified or blocked, and why.
A record of every decision, rule and test in your repository, updated with each commit, so a person or AI agent can continue the work.
The repository, yours: code, IP and history, with no license back to us. If you stop working with us, everything keeps running.
Every spec's acceptance criteria pass in your CI, functional and visual, and every change entered through a pull request with human approval recorded.
A 45-minute working session, then a week with the forward deployed engineer in your team. You leave with your current numbers, three specs from your backlog and the setup plan.
The factory produces the three specs as pull requests in your repository and CI. An engineer on your team approves each. The pilot is invoiced when all three are approved.
Specs go through the factory every month, measured against your week 1 numbers. It extends to more modules and repositories, a second pod, and the QA and legacy lines below.
A defined-scope build is a factory engagement that ends when the system goes live. You keep everything; the factory can restart with the next scope.
When AI generates code instantly, testing becomes the bottleneck. This line writes and repairs end-to-end tests that check real behavior. Available on its own, for software we did not write.
Software QA automation →For unsupported framework and language versions, and monoliths that block the roadmap. First, tests capture what the system does today. No phase goes live without proving the same behavior.
Legacy software modernization →A codebase your team already maintains, with its architecture and history.
Your language, framework and test tooling, from modern web products to enterprise platforms with years of history.
A legacy enterprise system in the public sector, on Dynamics 365 and .NET with years of history, under the client's CI and review flow.
Your repository, your CI, your environments.
We get only the access the work needs, and it ends when the engagement does.
They are kept in your secret manager and environment variables. Test results and screenshots are checked so they do not expose them.
Assistants make each developer faster inside the same process, where a person still interprets, places, writes and reviews every change. The factory takes each spec from start to finish and produces the change with its tests, a record linking it to its requirement, and proof that it works. Your engineers define what to build and approve the result.
An engineer on your team, or ours, reviews every change through a pull request with the reasoning, the diff, the tests and the evidence. That includes every change to a test suite.
An independent check that did not write the tests reviews every one of them for weakened assertions, skipped steps and emptied validations, and blocks what it finds. The same check covers the reference screenshots of visual tests.
Yes. The factory runs in production today on an enterprise system with years of history. When the platform itself is out of support, the legacy modernization line updates it first, with the tests written before anything changes: Legacy software modernization.
The unit is a product spec: a feature, a rule, an integration, a replacement. Large initiatives enter as a sequence of specs.
Everything the factory produces: the code, the tests, the records linking each change to its requirement, the proof that it works. It is in your repository from the first commit.
Define and approve. Your team writes the specs and their acceptance criteria together with the forward deployed engineer. Your team also answers the questions the factory raises and approves the pull requests. The factory produces the code, the tests and the evidence.
We work in time zones aligned with your working day. Your forward deployed engineer joins your standups.
If you build a Company Brain, one connected record of how your company works, what the factory learned about your system is already in it.
One 45-minute working session: we write three of your pending changes as specs and walk you through what the factory would produce for each.
You leave with the specs written and the setup plan.