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.
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.
Connect agents to the tools you already run
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.
Fix a deployment that stalled
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.
Who this is for
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.
Why model-agnostic matters more than which model
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.
Common questions
What does an AI agency actually do?
An AI agency scopes, builds and operates AI systems on your behalf. In practice that means identifying which parts of a workflow are worth automating, building agents that handle those steps, connecting them to the tools you already use, and monitoring them once they are live. The build is usually the easy part — keeping agents reliable in production is the work most teams underestimate.
Is this suitable for a startup, or only larger companies?
Startups and small teams are the core focus. Smaller organisations tend to see results faster because there are fewer systems to integrate and fewer approval layers between a problem and a deployed fix. If you have a repetitive, high-volume process and someone who can spare an hour a week to give feedback, that is usually enough to start.
How is this different from hiring a developer to use an AI API?
Calling a model API is a small part of it. The difficult parts are giving an agent reliable access to your data, handling the cases where it gets things wrong, deciding what it is not allowed to do, and being able to see what it did after the fact. A single API call has none of that. Agents that run unsupervised need observability and guardrails or they fail quietly.
What does model-agnostic mean, and why should I care?
It means your agents are not hardwired to one provider. Models change quickly — pricing shifts, capabilities move, providers deprecate versions. If your automation is welded to a single model, every one of those changes becomes your problem. Keeping the orchestration layer separate from the model means swapping the model is a configuration change rather than a rebuild.
How long before we see anything working?
It depends entirely on the process and how clean the surrounding data is, so any agency quoting you a fixed timeline before seeing your systems is guessing. What we can commit to is scoping a first workflow before you commit to a build, so you know what you are buying.
Do we own what you build?
Yes. The intent is handover, not dependency. Your team should be able to read, change and run what we build without us. If an agency's commercial model depends on you never being able to maintain your own automation, that is worth asking about directly.
Where are you based?
We work with clients internationally, with offices in Wilmington, Delaware; San Diego, California; and Cape Town, South Africa. Most engagements run remotely.