AGT
AI agents and workflow automation
Not a chat window. A member of staff that never sleeps and always writes down what it did.
The short version
Agents that carry a whole process end to end — reading the enquiry, checking availability, drafting the quote, escalating the parts a person should see.
Most businesses have bought a chatbot and found it answers questions nobody was asking. An agent is different: it does the work. We build agents that hold state across a multi-step process, call your real systems, and hand a person the decisions that need judgement.
What is included
Concretely, this is the work.
- Enquiry handling agents that read an inbound message, extract what is being asked, check your live data and reply with a real answer
- Quoting agents that assemble a price from your rules, margins and supplier costs, then route it for approval
- Multi-step orchestration where several agents hand work between each other with a supervisor deciding what happens next
- Escalation and handover rules so a person is brought in on exactly the cases that need one, with full context
- Evaluation harnesses that score agent output against real historical cases before it goes anywhere near a customer
- Audit logging of every decision, input and tool call, because in a regulated process you have to be able to explain what happened
Approach
What you should expect to change
- A measured share of enquiries resolved without a person
- Response time in minutes at 2am rather than hours the next morning
- The judgement cases reaching a human faster, with the routine already cleared
How we build agents
We start with the transcript, not the technology. Give us two hundred real enquiries and the replies your team sent, and we can tell you what fraction an agent can close, what fraction it can draft for review, and what fraction it must never touch. That split is the whole project — get it wrong and you either automate nothing useful or you embarrass yourself in front of a customer.
From there we build against your actual systems. An agent that cannot read your availability, your pricing rules and your customer history is a demo, not a deployment.
What we insist on
Every agent we ship has an evaluation set — real cases with known-good answers — that runs before every release. When a model changes underneath you, and it will, the evaluation tells you whether behaviour moved before your customers do.
Every agent has a documented escalation path. There is no configuration in which it silently guesses at something consequential.
And every action is logged in a form a non-technical manager can read. When someone asks why the agent quoted that price, the answer takes thirty seconds to find.
Questions about aI agents and workflow automation
Will it make things up?
An agent constrained to your own data and rules, with retrieval instead of recall, is a very different thing from a chatbot answering from memory. We also build the evaluation set that tells you the error rate before launch, and monitoring that tells you if it moves.
Which models do you use?
Whichever fits the task, the budget and the data-residency rules — commercial APIs where they are the right answer, self-hosted open-weight models where the data cannot leave your infrastructure. We build so the model is a swappable component, not a foundation you are welded to.
What happens when the model gets deprecated?
This is the question most agencies do not have an answer to. We isolate model calls behind one interface, keep the evaluation set current, and re-run it against the replacement. Migration becomes a scheduled afternoon rather than a crisis.
Also
Other things we build.
Booking and reservation platforms
The system your revenue actually passes through — search, availability, quoting, payment, confirmation, amendment, cancellation. Built to survive the edge cases.
Read more OPSOperations and back-office systems
The platform your team lives in all day. Suppliers, jobs, staff, documents, margins, reporting — shaped around how you actually work rather than a generic template.
Read more RAGPrivate AI over your own data
Search and answering across your internal documents, contracts, records and history — running where your data is allowed to be.
Read moreStart a conversation
Have a project like this?
Send us a paragraph describing what the system needs to do and what you are using now. We will come back with an honest view of whether it is a fit and roughly what it costs.