AI Agents
Deciding where an agent earns its place, and building the one that does.
Software that owns a step in your work: reading, deciding within its limits, and handing you what needs judgment.
Implementation. Built on the platforms you already run — Microsoft, Anthropic or OpenAI — and handed over with the off switch.
Operations, finance and HR leaders with process volume, and the IT teams who will have to support what goes live.
A queue keeps growing, the same questions arrive every week, or work is piling up behind one person who knows how it is done.
The practice
Opportunity assessment
Which processes an agent would genuinely change, what that is worth, and which are better served by plain automation.
Agent design
What it handles, what it escalates, what it must never do alone, and how it behaves when it is unsure. Designed before it is built, because this is what decides whether it survives.
Document and message understanding
Invoices, policies, contracts, email and forms read reliably enough to act on, including the layouts nobody has seen before.
Systems integration
Connected to the ERP, the finance system, the document estate or the service desk, with permissions scoped to the job it does.
Platform selection
Built on Microsoft, Anthropic or OpenAI according to where your data sits, what you already license, and what the process needs.
Evaluation and testing
Tested against real history and the awkward cases people remember by name, with an agreed standard for good enough before go-live.
Guardrails and audit
Boundaries enforced rather than implied, and a record of what it did and why that someone who was not there can read.
Adoption and measurement
The team around it trained, the exceptions owned, and a measured before and after on the numbers agreed at the start.
It acts, and it knows where to stop.
- 01Reads
The document, the message, the form — including layouts nobody has seen before.
- 02Understands
What kind of thing it is, and what matters in it.
- 03Acts
Within boundaries set before the build, on the systems it has scoped permission to touch.
- 04Records
What it did and why, in a form someone who was not there can follow.
Every process has a tail of cases that do not fit. An agent that cannot recognize its own limits will handle them confidently and wrongly, so the escalation path is designed first — and it is what makes the difference between production and a demo.
We work out where an agent earns its place, design the boundaries it operates inside, build it against your systems, and measure it against a baseline taken before the build. The platform is chosen during design — Copilot Studio, OpenAI or Anthropic — by where your data sits and what you already license.
That last part is what separates something you can put into production from something that demos well. Every process has a tail of cases that do not fit, and the value is in an agent that knows which ones they are.
Where an agent beats a rule
High volume, document- or message-driven, mostly rule-bound, with a stubborn exception tail. Where the inputs are consistent enough to write down as rules, plain automation is cheaper to run and breaks less often. An agent earns its cost where the inputs vary and something has to be understood before it can be actioned.
Built where your work already is
Microsoft, Anthropic and OpenAI all build capable agent platforms. Which one fits depends on where your data lives, what you already pay for, and what the process demands. That is a decision made in the design, not a preference we arrive with.
One process at a time
One process, a measured starting point, and an agreed definition of good enough. That is the only way anyone can say afterwards whether it worked — and a result you can point at is what funds the next one.
What you keep
The code and the data·Your existing relationships·Approval and control·The ability to stop·The off switchWhat that means
Built on the platform the work already sits on.
Which one fits is decided in the design, by where your data lives and what you already license. It is not a preference we arrive with.
- Copilot Studio
- Agents inside Microsoft 365, grounded in SharePoint and Dataverse, published into Teams. How we build them.
- OpenAI
- Where the work needs model capability outside the Microsoft estate, or a process has to run on its own terms.
- Anthropic
- The same, and where long-document reasoning and careful refusal behaviour matter to the job.
- Power Automate
- The orchestration around an agent: what it triggers, what it writes back, who it notifies.
Technology Strategy & Advisory
Strategy tied to what the business is trying to do, and the advisory weight to act on it.
Learn more ↗Data & Analytics
What to do with the data estate you have, and the platforms you are already paying for.
Learn more ↗Training
Process, automation and AI — taught to the people who have to do the work.
Learn more ↗Start with a 45-minute briefing.
No pitch. We’ll map your situation against what actually works and tell you honestly where to start.