Custom AI agents
Agents built around one job, done properly
An agent is not a chatbot with a nicer name. It is software that reads a situation, decides what to do, uses the tools you give it, and knows when to stop and ask a human. We build those — starting from a single job in your business that is repetitive, judgement-light and expensive in people's time, and expanding only once it works.
We run agentic behaviour in production: qualification that captures structured facts from a conversation, rule-based routing decisions, and automatic call summarisation onto records.
The engagement
What we deliver
Job definition
We start by writing down exactly what the agent decides, what it can touch, and what it must never do.
Retrieval over your data
The agent answers from your documents, catalogues and records — not from general internet knowledge.
Tool access
The agent can look things up, write records and trigger actions in your systems through controlled tools.
Guardrails and handoff
Explicit limits, plus a human in the loop wherever the cost of being wrong is real.
Monitoring after launch
Traces of what the agent did and why, so you can see its behaviour instead of trusting it blindly.
How it runs
From first conversation to live agent
- 01
Find the job
We look for one repetitive, text-heavy job with a clear right answer. Not a strategy deck — a job.
- 02
Design the agent
What it decides, what data it can read, which tools it can call, where it must stop and ask a person.
- 03
Build and connect
The agent, grounded in your own data, wired into the systems it needs — CRM, WhatsApp, sheets, accounting.
- 04
Deploy with a human in the loop
It goes live handling a slice of the work, with a person reviewing what it does before the loop widens.
- 05
Operate and improve
We watch traces of real behaviour and tighten it. An agent is a system you run, not a project you finish.
FAQ
Common questions
Where should we start?
With one job, not a platform. The best first agent is a task your team repeats daily, where the inputs are text and the rules are known. We help pick it in the first conversation.
Will the agent make things up?
That is the main engineering problem, and it is why we ground agents in your own data, constrain what they can say and do, and put a human in the loop wherever a wrong answer costs money.
Do you replace our staff?
No. Every agent we run in our own products hands off to a person. The realistic outcome is that your team stops doing the repetitive part and handles more of the work that needs them.
Also from our AI practice
Thinking about custom ai agents?
Tell us the job. We'll tell you honestly whether an agent should do it, and what it would take to run it properly.