Service 01
Agents that do the work.
An agent that answers questions is a chat window. An agent that books the appointment, files the ticket and updates the record is a colleague. The difference is not the model — it is the tools, the boundaries and the evaluation around it.
- Timeline
- 4–8 weeks
- Suited to
- Teams with a repetitive rule-shaped task that eats hours every week
Typically starts at
Priced in your currency and fixed in writing before work starts. Change the region in the navigation to see local pricing.
Start a ProjectSee what each package includes01The problem
Why most agent projects stall
The demo takes an afternoon and everyone is delighted. Then it meets real data, real edge cases and real consequences, and nobody can explain what it did or why. Most agent work dies in the gap between the prototype and something you would let near a customer.
It was never scoped to a job
An agent asked to handle anything handles nothing reliably. Without one clearly bounded task, there is no way to say whether it works.
It can talk but it cannot act
Wired to a model but not to your systems, it produces confident text that someone still has to copy into the CRM by hand.
Nobody can see what happened
When it gets something wrong — and it will — there is no trace, no replay and no way to tell whether the fix worked.
02Our approach
How we approach it
We build the evaluation before we build the agent. Once there is a set of real cases with known-good outcomes, every change becomes measurable instead of a matter of opinion.
One job, drawn tightly
We pick a single task with a clear definition of done, ship it properly, then widen the scope from evidence rather than ambition.
Tools, not just prompts
The agent gets typed, permissioned access to the systems it needs — your CRM, your calendar, your database — with every call validated at the boundary.
Human handoff by design
Explicit confidence thresholds, and a defined path to a person for anything above the risk line. Escalation is a feature, not a failure.
Observable in production
Every run traced: inputs, tool calls, decisions and cost. When something goes wrong you can see it, replay it and prove the fix.
A request enters the agent loop. The agent calls a typed, permissioned tool layer, which validates every call before it reaches your systems — CRM, calendar, database — and validates the response on the way back. Each result passes a confidence gate: above the threshold the agent completes the task, below it the run is handed to a person with its context attached. An evaluation set of real cases with known-good outcomes gates every change before release, and tracing records the inputs, tool calls, decisions and cost of every run.
03Deliverables
Exactly what you receive.
No vague line items. This is the list that goes into the agreement.
Foundations
- Task scoping and a written definition of done
- Evaluation set built from your real cases
- Tool and permission map across your systems
- Risk assessment and escalation thresholds
Build
- Agent implementation with typed tool access
- Input and output validation at every boundary
- Retry, fallback and human-handoff paths
- Tracing, cost tracking and run replay
- Regression suite that runs on every change
Handover
- Deployment and monitoring setup
- Evaluation results against the agreed benchmark
- Runbook for failures and rollback
- Working session with the team who will own it
04How it runs
Five stages, each with a clear exit.
- 01
Scoping
We find the one task worth automating first — high volume, clear rules, low blast radius — and write down what success actually means.
- 02
Evaluation
We assemble real cases with known-good answers before writing agent code. This is the step almost everyone skips, and the reason almost everyone gets stuck.
- 03
Build
Tools, validation and the agent loop, measured against the evaluation set from the first commit.
- 04
Hardening
Adversarial cases, rate and cost ceilings, failure paths and the human handoff. We try to break it before your customers do.
- 05
Handover
Deployed with tracing in place, plus the runbook and the session your team needs to own it.
05Related work
How this looks in practice.
06Questions
AI Agents, answered.
Whichever fits the task, and we keep that swappable. The model is the easiest part of the system to change and the least interesting part of the decision — the tools, the evaluation and the boundaries are what determine whether the agent works.
Sometimes, which is why nothing is trusted on the model's word alone. Outputs are validated against schemas, actions are constrained to permissioned tools, and anything above the confidence threshold goes to a person. We report measured accuracy against your evaluation set rather than promising it will not happen.
No. We use providers on terms that exclude training on your data, and we will show you the configuration. If your data cannot leave your infrastructure, say so at scoping and we will design for that instead.
We instrument cost per run from day one and give you the projection before launch, along with the ceilings that stop a runaway loop turning into a bill.
AI Agents
Let us look at your project.
Send the brief — or just the problem. We will tell you what we would do and what it would cost.