AI agents & automation
Beyond chat: AI that does jobs.
Triaging inbound email. Drafting quotes. Chasing debtors. Summarising meetings into actions. We build and run practical automations that pay for themselves in hours saved.
What good looks like
The difference between AI that works and AI that disappoints is how tightly the job is defined.
The wrong brief
“An AI employee” that does everything. Too vague to test. Too vague to prove.
The right brief
“Respond to every inbound lead, collect the missing qualification details, & route the opportunity to the right person.” Specific enough to test – and specific enough to prove.
How we build an agent you can trust
An agent is only as good as what it knows & what it is allowed to do. Before we automate anything, we do the groundwork – the same discipline we bring to your security & backups.
Map & build
Map the process – trigger, steps, systems, handoffs & exceptions. Then build the knowledge foundation: your offers, rules & past decisions. Without that, AI is guessing.
Set the boundaries
What it can read, what it can change, what needs your approval & what it must never do. Then we test the messy paths – missing data, conflicting instructions, an out-of-scope request.
Launch & keep it honest
Human in the loop first – drafts & recommendations, hands-off only once it has earned it. Every agent gets an owner, monitoring & a regular review. Left alone, an agent drifts.
Where an agent earns its keep
The test is simple: is there a question, or a job, that comes up over and over and always lands on the same person?
The repeat question
Leave balances, the process for a new starter, where a policy lives. Answered from your actual documents, instantly, at 7pm, without interrupting anyone.
The repeat job
The monthly report somebody rebuilds by hand. The inbox triage. The quote that gets assembled the same way every time. Built once, then it runs.
Version 1, then grow
Most attempts at this die the same way – somebody clever builds something, it half works, they get busy, and six months later nobody knows how it hangs together.
So we start with one agent that solves one annoying thing. Not a platform. Not a transformation programme. We set the baseline before we start, then report what changed – response time, quote turnaround, completion rate, backlog and errors. Real numbers from your business, not a demo.
You will also want to know the running cost up front. Agents consume credits when people use them, so we set a cap and tell you what it will cost before you commit.