Engineering

When off-the-shelf does not fit, we build it

Bespoke agents, automations, and scripts in TypeScript or Python — engineered for the exact problem your operation has, not the generic version of it.

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Built for your exact problem

A lot of "AI products" are a thin wrapper around a model with someone else's assumptions baked in. When your problem does not match those assumptions, you are stuck. We build the real thing: agents and automations written and owned as proper software, fit to your data, your edge cases, and your definition of correct.

If the right answer is a small, sharp script rather than a sprawling platform, that is what you get. Simplicity is a feature.

// scoped to one workflow — your data, your rules
const agent = new Worker({
  role: "ops-monitor",
  sources: yourDataSources,
  rules: yourEscalationRules,
  memory: "verified-facts-only",
});

await agent.run(); // logs every action

What's included

Every custom build ships with these

TypeScript or Python

Built in the language that fits your stack — typed, linted, and structured so any developer can pick it up after handover.

Your data, your rules

Scoped to your data model and edge cases from day one — not a generic template with your variables swapped in.

Versioned and owned

Real software — Git-versioned, code-reviewed, documented. Not a black box only we can maintain. You own what we build.

Human review gate

Nothing touches a live system without a human approval step built in — failures surface for review, they do not silently propagate.

Live path verified

Failure reproduced in a test before a fix is written. "Code compiles" is not shipped — the live user-visible result is shipped.

Full handover docs

Deployment steps, architecture notes, and working code you own. We are not building a dependency — we hand over the keys at the end.

How it works

Step 1

Scope the problem

We work out the exact input, output, and constraints before writing a line. If we disagree with the approach, we say so. Simplicity first.

Step 2

Build and review

We build the agent or automation, review the code internally, and test it against your actual data and edge cases before showing you anything.

Step 3

Live path verified

We run the production path and confirm the user-visible result is correct. "Code compiles" is not shipped — the live result is shipped.

Step 4

Handover

You receive the working system, all source code, documentation, and deployment instructions. We are not building a dependency — you own what we build.

Self-improving builds

Custom builds we deliver are designed to improve from operational feedback. Failure logs are structured so the agent can reason about what went wrong. Where appropriate, the agent proposes its own corrections — which go through a human review gate before being applied.

This means the build continues to get more accurate after it ships rather than being a static artefact that degrades as your environment changes.

Common questions

Custom AI Builds FAQ

A focused single-workflow agent or automation: 3–7 business days from a clear scope. A multi-agent pipeline with integrations, a review gate, and staging deployment: 2–3 weeks. We scope it before committing to a date — the discovery call is where we size it, and we tell you if the answer is simpler than you think.
Three things: the exact input the agent will receive, what correct output looks like, and at least one real example of a case it must handle correctly and one it must refuse or escalate. Access to sample data speeds things up significantly. We ask more questions during scoping — but that is the minimum to price and scope accurately.
Builds we deliver are structured for self-improvement: failure logs are machine-readable so the agent can surface what went wrong, and the correction goes through a human gate before being applied. Your own team can extend it — the code is clean and documented for that purpose. We also offer a retainer for ongoing changes if you prefer not to maintain it in-house.
Yes — BYOK is the default. Your Anthropic, OpenAI, or Z.AI keys go into your own infrastructure; we never hold credentials you do not want us to hold. If you prefer a managed setup where we handle the API billing and rotation, that is available too. Either way, the keys are stored encrypted at rest and are never written to source code or config files.
No-code platforms encode someone else's assumptions about what your workflow looks like. When your problem does not fit those assumptions you hit a wall — and you are stuck inside a product you do not own. What we build is proper software: you get the source, the tests, and the deployment config. It does exactly what your operation requires, handles your edge cases, and can be extended by any developer without licensing a platform to do it.

Describe the exact problem

Tell us what you need built, what has not worked, and what correct output looks like. We will scope it and tell you if a simpler approach exists.

Get a quote See the work