We are an AI agency that builds and runs AI agents for small and mid-sized teams. Not slide decks about AI strategy, and not a pilot that stalls after the demo — working automation that handles real volume, that you can watch, and that your team can keep running after we hand it over.
Most requests fall into one of three shapes.
You have a process that eats hours every week — support triage, quote generation, document review, onboarding checks. We scope it, build an agent that handles the repetitive path, and route the exceptions to a human. The point is not to automate the whole job; it is to automate the eighty percent that is identical every time.
An agent that cannot reach your CRM, inbox or database is a chatbot. Integration is where most of the real engineering sits, and it is the part that determines whether the thing is useful or a demo. See pricing for how integration work is scoped.
A large share of AI projects never make it from pilot to production. Usually it is not the model — it is missing observability, no error handling, unclear ownership, or nobody able to say what the agent did last Tuesday. This is often cheaper to fix than to restart.
This works best if you have a repetitive, high-volume process, some tolerance for iteration, and someone internally who can give feedback on whether the output is actually right. It works badly if you need a fixed-scope, fixed-price deliverable signed off in advance — agent behaviour is discovered by testing it against real inputs, not specified upfront.
If you are an enterprise buyer with procurement, security review and compliance sign-off, that is a different engagement shape — start at contact and say so.
Model choice is the least durable decision in any AI build. Pricing changes, context windows grow, providers deprecate versions, and the best model for a task in one quarter is rarely the best in the next. Agents built directly against one provider inherit every one of those changes as rework.
Keeping orchestration separate from the model means switching is configuration, not a rebuild. If you want the background on how agents differ from simple model calls, IBM and Google Cloud both have solid primers. For a structured way to think about risk before you deploy anything autonomous, the NIST AI Risk Management Framework is the reference most teams end up using.
Bring us the workflow — support triage, quoting, document review — and we'll show you what an agent running it looks like, with the observability and governance to keep it running.