AI that has a job, and a number attached to it.
Most AI projects fail because nobody defined what would change. We start from the workflow that is costing you hours, build the agent that removes it, and measure it the way you measure any system: latency, failure rate, cost per task.
If a workflow does not get measurably faster or cheaper, it does not ship. That is the only test that matters.
A test set with real cases and a pass bar, so you can tell whether a prompt change made it better or just different.
Token spend attributed to the workflow it serves. AI without unit economics is a bill nobody can defend.
The specific things we build under this.
Task agents
Agents that own a whole job end to end — triage a queue, draft the reply, update the record — with the tools scoped tightly enough that failure is recoverable.
Workflow automation
The unglamorous kind: information moving between systems that were never meant to talk, without a person as the integration layer.
Retrieval over your own data
Your documents, tickets and history made answerable, with citations back to the source so the answer can be checked rather than trusted.
Content systems
Engines that produce in your voice and your format at volume, with a review step, because unattended publishing is how brands get embarrassed.
Lead and enquiry handling
Follow-up that never misses, qualification that runs at 2am, and routing that puts the right context in front of whoever picks it up.
Evaluation harnesses
A graded test set per workflow. This is the difference between shipping AI and hoping about it, and it is the first thing we build.
Human-in-the-loop design
Deciding what the agent may do alone, what needs approval, and how a person takes over mid-task without losing state.
Observability
Every run traced — inputs, tool calls, cost, outcome — so a regression is something you can look at instead of argue about.
What we hold to, whatever the brief says.
Narrow beats clever
An agent with three tools and a clear boundary outperforms a general one, and you can reason about what it will do.
Failure paths first
The model will be wrong. The system is designed around that — retries, fallbacks, and an escalation that reaches a person.
Your data stays yours
Retention, residency and training-use settled in writing before anything is wired up.
Not married to a model
Provider behind an interface, so a better or cheaper model is a config change rather than a rewrite.
From first call to running in production.
- 01Find the workflowWhere the hours go, and which part is genuinely mechanical.
- 02Build the evalReal cases with correct answers. The bar is agreed before the build.
- 03PrototypeThe smallest agent that clears the bar, run against the real test set.
- 04Wire it inInto your tools, with permissions, approval steps and audit trail.
- 05Measure and tuneCost, latency and failure rate watched, then driven down.
Where we've done this before.
Before you ask.
Is our data used to train models?
No. We configure zero-retention endpoints where available and settle residency and retention in the contract before anything is connected.
What if the AI gets it wrong?
It will, sometimes. That is why every build starts with an eval set and ends with a defined escalation path to a human. The question is not whether it errs, it is whether the system handles it.
Where should we start?
One workflow with an obvious cost. Something a person does more than twenty times a week with a predictable shape. Breadth after the first one pays.
Need ai agents & automation?
Tell us the problem rather than the solution. We will tell you whether this is the right service for it, including when it isn't.