Your team adopted AI coding assistants and each developer got faster. Every change still needs someone to interpret the requirement, place it in the codebase, write the tests and review all of it, so the cost per change stayed flat.
A factory changes that. The pipeline produces the code, the tests and the first round of verification, and engineering time goes to deciding what to build and approving what lands.
Tricentis found in June 2026 that 30% of organizations say AI-generated code has grown too large for their teams to test fully.
The input is a specification the way product writes it: what should happen, the acceptance criteria, and the scenarios. It describes behavior, and the factory decides the implementation.
The pipeline reads your architecture before touching it, so the code lands where your team would have put it, following your conventions.
Each criterion maps to a test that exercises real behavior, including the failure paths.
On desktop and mobile, with evidence you can inspect.
Which requirement produced which file, and which test covers it.
What was produced, what was verified, what failed and was corrected along the way.
The fastest way to make a test pass is to make it check less. Every test the factory produces is reviewed by a verifier that did not write it, looking for assertions loosened, steps skipped, and validations emptied. What looks like that gets blocked, and this applies to visual baselines too.
The factory produces a pull request with the reasoning, the diff, the tests and the evidence, and stops. An engineer approves what enters your codebase, every time.
A spec with a hole produces a precise question, and that task waits until someone answers it.
We study your architecture, your conventions and your CI. The factory is built on our harness and adapted to your project: your layers, your test stack, your pipeline.
The first specs run with your team watching. Your engineers see where the code lands and how the tests are derived, and correct the line before trusting it.
Specs flow monthly. Each one comes out as a pull request with tests and evidence, and your team, or ours, approves.
New spec types widen the line without rebuilding it. The same pipeline keeps producing as the backlog grows.
The changes, shipped: code in your repository, in your conventions, merged through your review.
The test suites, functional and visual, running in your CI on every change, with evidence attached to every run.
The traceability record per spec: requirement to file to test, auditable without asking anyone.
The pipeline logs: what ran, what was verified, what was blocked and why.
Every spec's acceptance criteria pass in your CI, functional and visual, and every change entered through a pull request with human approval recorded.
The factory produces changes in a codebase your team already maintains: real architectures, with history.
GitHub Actions, GitLab CI, Jenkins, Azure Pipelines, CircleCI.
The line is adapted to your language, framework and test tooling as part of setup.
The human gate runs inside your existing pull request process.
Everything the factory produces is readable code, tests and records in your repository, under your version control. If you stop working with us, everything it produced keeps running.
A defined scope, delivered working. It ends when the system ships.
AI Software Delivery →We install the foundations in your repository, prove them by shipping, and leave. Your engineers produce from then on, with their own tools.
AI-Ready Codebase →Your team writes specs and approves pull requests. We run the line, continuously.
The two engagements share foundations and differ in who operates afterward. Codebase is an enablement: it transfers the capability and ends. The factory is a service: it produces every month. Teams move between them in both directions, and a factory client keeps everything needed to take the line's output in house.
Your repository, your CI, your environments.
And it ends when the engagement does.
Through your secret manager and environment variables, and run evidence is checked so it does not leak them.
Assistants make each developer faster inside the same process, where a person still interprets, places, writes and reviews every change. The factory takes the spec end to end and produces the change with its tests, traceability and evidence, and your engineers define and approve.
An engineer, on every change, through a pull request with the diff, the tests and the evidence. The factory never merges its own work.
An independent verifier that did not write them reviews every test for weakened assertions, skipped steps and emptied validations, and blocks what it finds. The same check covers visual baselines.
The factory needs a codebase it can read and a CI to run in. If yours needs modernizing first, that is 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 traceability records, the evidence. It is in your repository from the first commit.
Define and approve. Your team writes specs with acceptance criteria, answers the questions the pipeline raises, and approves pull requests. Producing the code, the tests and the evidence is our job, which is the difference with AI-Ready Codebase, where your team is the one producing.
One working session: we take three real changes your team has pending, write them as specs, and walk you through exactly what the line would produce for each. You leave with the specs written and the setup plan.
Everything the factory learns about your system stays as an engineering brain you keep. If you later grow a Company Brain, this is already a module of it.