AI-native product engineering
Turn repetitive work into
software you can trust
We help operations teams automate document intake, support triage and internal knowledge workflows — using AI where it improves the result and ordinary software where it does not. US-led engineering, from a scoped assessment through deployment and handover.
Three required fields. An engineer reads it — there is no qualification call first.
Something you can check, right now
We had to run untrusted code without a server to attack
So we wrote two teaching runtimes that execute a learner's C and Python in their own browser. No queue, no container per submission, no per-run cost — and the mistakes a real compiler lets through get explained at the line they happen.
It is our own product, not a client reference. Its value to you is that you can open it and judge the engineering yourself, without an account.
What is true, and how you would check it
No server executes visitor code, so the per-run cost is zero at any traffic level.
The runtimes are client-side modules; there is no execution endpoint to call.
The same program produces the same output every time, which is what makes automated checking possible.
rand() is seeded and the clock is fixed; the test suite asserts repeated runs are identical.
An automated test suite covers the runtimes, the checker and the exercise content.
The repository’s own automated test suite, which anyone with the source can run.
What we take on
Three workflows that quietly cost you a person
Documents arrive and somebody retypes them
Invoices, claims, forms and attachments come in by email and portal, and a person opens each one, reads it, and copies fields into a system that will not accept them in the shape they arrived. The cost is not the typing; it is the exceptions, the duplicates and the ones that get missed.
Usually looks like: Extraction with a schema, a quarantine for what does not fit, and a human where it is genuinely ambiguous.
Requests go to the wrong queue and sit there
A request is routed on a keyword, lands with the wrong team, waits a day, and reaches the right person having already annoyed the customer. Nobody records how often the first routing was wrong, so nobody can say what it costs.
Usually looks like: Classification with a confidence it will act on, uncertain cases handed to a person, and a reason attached to every decision.
The answer exists, in a document nobody can find
The policy is written down. It is also written down three more times, in versions that disagree, and the current one is not obviously the current one. People ask a colleague instead, and the colleague is sometimes wrong.
Usually looks like: Answers grounded in a specific document and version, permissions enforced before content is read, and a refusal when the evidence is not there.
Not on the list? Describe yours — we will tell you which parts we think are actually hard, including when the answer is that you do not need us.
Fixed scope · agreed before we start
Three ways to begin, in the order that usually works
Assessment when the scope is uncertain, a bounded pilot once a workflow is understood, ongoing delivery when the work justifies it.
Typically about a week — confirmed in the written scope
Workflow Assessment
You know a workflow is costing you and you are not yet sure what to build, or whether AI belongs in it at all.
Typically a few weeks — confirmed in the written scope
Bounded Pilot
A pilot with fixed limits (that is what “bounded” means): one workflow, already understood, built and deployed to a training environment with evaluation and a rollback path.
monthly · senior-led
Ongoing Delivery
A pilot proved the thing works and the work is now continuous: more workflows, more integrations, and somebody accountable for keeping it running.
No published price, because we have not set one we could stand behind for every scope — each offer says what determines its fee, and you get a fixed quote before anything starts. How an engagement runs
Proof you can inspect
Systems we built, and what they do not do
Our own products — client work is not ours to publish. Each one states its constraints, how you would verify the results, and where it falls down.
We also teach this, in the open
A project-based curriculum with AI-assisted code review, and exercises that run in your browser with no account. Separate business, separate infrastructure — no learner ever touches client work.
Hiring instead of outsourcing?
Contract and permanent placements from AI engineers to fractional CTOs — screened by people who build software, not keyword-match it.
Looking for project work yourself? See careers
Start here
Tell us what happens today.
What the workflow is, who does it, and how you would know it had got better. An engineer replies with questions or a view on scope — and will tell you if we are not the right people for it.