AI & Agents
Agents that don't just answer.
They do the work.
We build production AI agents — chat on the surface, tool use underneath, with approval gates and an audit trail so they act only within limits you set.
From prototype to production.
Customer-facing chat agents
Agents that answer real questions, qualify intent, and hand off to a human with the full conversation attached.
- Grounded in your content
- Lead capture built in
- Escalation with context
Internal workflow agents
Multi-step agents that triage requests, draft responses, update records, and close the loop across systems.
- Runs multi-step plans
- Reads and writes to your systems
- Reports what it did
Tool use & integrations
Agents that actually do things — call your APIs, query your database, file the ticket, book the meeting.
- Typed tool definitions
- Retry and failure handling
- Least-privilege credentials
Guardrails & approvals
Consequential actions pause for a human. Everything the agent does is logged and reviewable.
- Human-in-the-loop gates
- Action allow-lists
- Full audit trail
Evaluation harnesses
A test suite for your agent. We measure behaviour on your real scenarios before and after every change.
- Scenario-based evals
- Regression checks on prompts
- Measured, not vibes
Cost & latency engineering
Model selection, prompt caching, and routing tuned so you know the per-interaction cost before go-live.
- Prompt caching
- Model routing by task
- Predictable unit economics
The win is the reassignment that never happens.
Most tickets are not slow because someone worked slowly. They are slow because they sat in the wrong queue for a day first. Classification on arrival is unglamorous and it is where the time goes.
Two reassignments before it lands.
- Service Desk
- Network Ops
- Back to Service Desk
- Desktop Support
Classified on arrival. No reassignment.
- Service Desk
- Desktop Support
Illustrative, not measured. What an agent removes is the reassignment — the ticket that sat in the wrong queue because nobody read it properly on arrival. We will not publish a time saving until it is your instance and your numbers.
Small bets first. Then scale what holds.
Pick the use case
- Find the highest-volume, lowest-risk task
- Agree what "good" looks like, in writing
Prototype
- A working agent on your real scenarios
- You test it before anything is committed
Harden
- Guardrails, approvals, and audit logging
- Evals wired into the deploy path
Run & expand
- Monitor behaviour and cost in production
- Take the next use case once this one holds
This site is the demo.
We can't show you another client's agent — those are behind their logins. So we put ours in public. The agent in the corner of this page, the voice agent on the homepage, and the one that will call you back from our agent page are all built by us on the same stack we'd use for you.
Ask it something hard. That is a more useful reference than a case study you can't verify.
See what we've built- What task, exactly?The narrower the first agent, the faster it earns trust.
- What systems must it touch?Tool use is where most of the engineering lives.
- What must never happen?That defines the guardrails before anything else.
- How will we know it works?We turn that answer into the eval suite.
A chatbot answers. An agent acts — it can call your systems, take multi-step actions, and finish a task. Most useful projects need both: conversation on the surface, tool use underneath.
This is an agent reading a real ticket.
A messy, angry, vague inbound ticket — the kind your desk actually gets — classified, prioritized, routed, and answered. The priority came from the impact × urgency matrix in code, not from the model's mood.
The demo is live on our work page. Paste your own worst ticket and watch the same pipeline run.
Run your own ticket
Have a use case in mind?
Tell us what you want an agent to do. Within two working days you'll have an approach, a prototype plan, and the per-conversation cost worked out.