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CodeHero

production AI

AI development company: your first workflow live in 21 days

We put AI inside the systems that already run your business. An agent runtime in Go, typed tools into your ERP, your CRM, your ticketing queue and your mainframe, an approval step on anything that writes, and an eval suite in CI that fails the build when the answers get worse. Any stack, any industry, from one workflow to a platform your own engineers run.

Get a quoteFour screens, about ten minutes. An engineer reads it and writes back, usually the same day.

21 days to the first workflow in production

The struck-through row is the part most pilots skip, and it is why nobody there can tell you whether the model got worse last week.

Prototype

A notebook that works on demo data and one closed vendor API.

  • spec.docxWord
  • proof-of-conceptPython · notebook
  • vendor APIclosed
  • evalnone

Production

A service with tools it can call, tests it can fail, and traces you can search.

  • agent runtimeGo
  • tool layerTypeScript
  • eval harnessCI gate
  • tracesOpenTelemetry

21 days to the first workflow in production

The struck-through row is the part most pilots skip, and it is why nobody there can tell you whether the model got worse last week.

How the work goes

Same order on every project. The first two steps are what make the third one provable.
  1. 01days 1–2

    Pick one workflow

    One process with volume and a right answer. We measure what it costs you today in minutes and errors, because that number is what the result gets judged against.

  2. 02days 2–6

    Build the eval set first

    A few hundred real cases out of your own history, each with the answer your people gave. It is the unglamorous part of the project and the reason everything after it can be proved instead of argued about.

  3. 03days 5–16

    Build the runtime and the tools

    Agent runtime in Go, typed adapters into your systems, an approval step on anything that writes. Evals run on every commit from day one.

  4. 04days 14–21

    Ship it and hand it over

    Traces, a runbook, and a day with your engineers so they can change prompts and tools without calling us. The handover is part of the price.

What the platform does, what a person does

The platform carries the mechanical half at machine speed. Our engineers take the decisions that cost money when they go wrong.
On a documented codebase the platform carries more of this, and on a twenty-year-old one our engineers carry more. The date we gave you holds either way.
What the platform does, what a person doesWho does itNotes
Reading the codebase and finding the integration pointsPlatformHours on a repository a person would spend a week reading.
Choosing which workflow goes firstEngineerVolume, a checkable answer, and someone on your side who owns the outcome.
Writing tool adapters to your APIsBothThe platform drafts from the API contract. A person fixes what the contract lied about.
Building the eval set from your historyBothPulling the cases is automatic. Deciding which past answers were correct is not.
Running regressions on every commitPlatformA drop fails the build, the same way a broken unit test does.
Deciding what the agent is allowed to write toEngineerYour risk, your call, written down before anything runs.

Who you get on the project

Dozens of migrations behind us and more than ten years spent inside other people's systems. The engineers who put Go and TypeScript services into production next to COBOL, RPG, VB6, Delphi and PHP are the same ones who build the agent layer. That is why our AI ends up inside the systems that carry your money instead of sitting beside them.

Give us read access and the platform produces a module map and a dead code list for your own codebase within days. You keep that document whether or not you hire us, and it is the fastest way to see how we work.

Questions we get from engineers

Is this a wrapper around a chat API?
There is a model in it, in the way there is a database in your billing system. The work is the tool layer, the approval gates and the evals that tell you when a model update made things worse. Calling an API is the cheap part; knowing when the answer is wrong is what you are paying for.
What happens when the model changes underneath us?
The eval suite runs against the candidate model before anything switches. If the score drops, you stay on the old one until the prompts and tools catch up.
Where does our code run while you work on it?
Wherever you require, including entirely inside your own network or cloud account. We have worked under client VPNs, on air-gapped build machines and on hardware that never left the building, and the specifics go into the contract.
Who owns what you build?
You do. Ordinary Go and TypeScript repositories, ordinary libraries, no runtime call back to us. Our platform is how we work; it is not something you end up renting.
Can our team maintain it after we finish?
That is what the handover is for, and it is why the tool layer is typed and the prompts live in version control instead of a vendor console. Your engineers change a tool definition, rerun the evals and ship, without us in the room.

Send us the workflow

Name the process you would automate first and roughly what it costs you when it goes wrong. Back comes how we would build it, how long it takes and what it costs, inside two working days.

Get a quote

Four screens: the system, what hurts, where you want to be, how to reach you. No call gets booked unless you ask for one.