SERVICE — AI
AI agents that earn their place.
AI agent development for teams with a process worth automating
Most AI agent projects die in the same place. They demo beautifully on clean inputs, then meet a real process with real exceptions and get quietly switched off. As an AI agent development company we build the other kind: agents scoped to one job, connected to the systems your team already uses, and clear about where they stop and a person takes over.
Part of Build and Transform — every engagement starts with an audit
When an agent is the right answer
An agent is worth building when a person is repeating a judgement call often enough that the pattern is learnable, and the cost of getting it slightly wrong is recoverable.
That second condition matters more than it sounds. Qualifying an enquiry, drafting a first-pass reply, flagging a piece for a second look: get one of those wrong and a human catches it downstream. Approving a payment sits in a different category, and we will say so when the thing you are describing belongs there. The agents that last remove a bottleneck without becoming a new single point of failure.
What we build
Qualification and intake agents
Conversational flows that collect what your team needs before anyone joins the thread, so a request arrives structured instead of as a one-line email. The pattern applies wherever intake currently happens across phone, email and messaging apps with no single record of what was asked for.
Back-office and workflow automation
Agents that read, route, and act on records a team currently moves by hand: orders, invoices, inspection reports, support tickets. Often the win is not the decision but the twenty minutes of copying around it.
Retrieval grounded in your own material
Answers drawn from your documentation, catalogue, and records instead of a general model's recollection. We handle ingestion, chunking, and retrieval so the agent cites something real, and says it does not know when nothing relevant comes back.
Multi-step agents with tool access
For jobs a single prompt cannot finish: agents that plan a sequence, call your internal APIs, and check their own output before returning it. Scoped tightly, because broad permissions plus vague instructions is a support ticket waiting to happen.
A defined human handoff
Every agent we ship has a written boundary: what it decides alone, what it escalates, what a person signs off. Teams skip this because it feels like admitting the agent is incomplete. It is the difference between a feature and a liability.
Evaluation and monitoring
A test set built from your real traffic, not sample data, plus logging that surfaces quality drift. Agents degrade quietly when a model updates or your inputs shift. You want to know before your customers do.
How we work on this
001
Find the process worth automating
Not everything should be an agent. We look at what your team repeats, how often, and what it costs when it goes wrong, then pick the case where an agent beats the current process by a margin you can measure.
002
Build against real inputs from week one
We wire the agent to your real data and your ugliest edge cases immediately. A demo built on tidy sample inputs tells you nothing about a Tuesday afternoon when someone pastes in a half-finished spec.
003
Ship it with a boundary and a dashboard
It goes live with escalation rules written down and behaviour visible. Then we watch the first weeks of real traffic with you and tighten it, because a first version is a hypothesis however well it tested.
FAQ
Questions we get asked about this.
What's the difference between an AI agent and a chatbot?
A chatbot answers. An agent acts. A chatbot can tell a visitor your lead times. An agent checks the schedule, works out whether the order fits, and puts a structured request in front of your sales team. The distinction that matters commercially is tool access: whether it can reach into your systems and do something, or only talk about them.
Which models do you build on?
Whichever fits the job, and we keep it swappable. Most production work sits behind an abstraction, so the underlying model can change without a rewrite. That has already saved clients money as pricing moved. For sensitive data we will talk through what can run in your own environment and what has to leave it.
How do you stop an agent from making things up?
You cannot eliminate it, so design for it. Ground answers in retrieved material instead of model memory. Keep scope narrow enough that the model is not improvising. Build an evaluation set from real inputs, and give the agent a clear way to say it does not know. Then put a human in the loop wherever being wrong is expensive.
How long does an agent project take?
Most run 4–12 weeks, depending on how many systems it touches. A single well-understood conversation is at the short end. Anything writing to several internal systems takes longer, and the extra time goes into integration rather than into the agent itself.
What happens when the model provider changes their API or pricing?
A normal maintenance question now, not an edge case. Keeping the model behind an abstraction is most of the answer. The rest is us staying involved after launch. We treat provider changes like any dependency update: our problem to flag, not yours to discover.
Other services
Start with the audit
Tell us how your operation runs today and where it hurts. We will tell you what the audit would cover and what you would have at the end of it — before you commit to anything.
Start with the audit Fixed scope, fixed price — you keep the report either way