AI Agent Development Company
An agent is software that decides which tools to use to get a task done. The useful ones are supervised: they work with a strict, permissioned toolset, every step is logged, and a person approves anything risky. We build those, and you can watch one run on our Labs page.
Is this you?
You are probably here because…
- 01A process has many steps across several systems and the same people do it every day.
- 02Fixed automation breaks whenever there is an exception.
- 03You want autonomy but your IT or risk team will not accept a black box.
- 04You have a prototype that cannot be trusted with real actions yet.
How we approach it
We write down the real steps, the systems touched and the points where a human must decide. The agent is only given what the job needs.
Limits such as maximum amounts or allowed actions are enforced in code, not in the prompt, because a rule in a prompt can be talked around.
Agents start with a human watching, with every tool call streamed and logged. Autonomy widens only as the record justifies it.
The services behind it
Each one has its own page with deliverables, timelines and tools.
Live products behind it
Working systems, not slides. Each page has the detail and a button into the live demo.
Related reading
Questions
An autonomous agent with deep system integrations typically takes 3–8 weeks from the mapped workflow to supervised production runs. A narrower agent can be quicker; the scope is fixed during discovery.
Three layers: a strict list of tools it may call, limits enforced in code outside the model, and approval checkpoints for risky steps. Our prompt-injection challenge on Labs shows why the code-level limit is the one that holds.
Yes. Each search, fetch and API call is logged with its inputs and result, and the run can be replayed. That record is what lets your IT and audit teams approve it.
No. We compare Claude, GPT, Gemini and open-weight models on your tasks and choose on accuracy, latency and cost per call, not on vendor benchmarks.