Service
Put the agent where the work is
More than a chat window: retrieval over your own documents and records, tool access into the systems that hold them, evaluation that tells you when quality moves, and a review path for the calls a person should still make.
The Problem
The demo works. The deployment does not.
Projects stall in the gap between a prototype that convinces a room and something you would put in front of customers or staff. The failure is rarely the model.
- 01
Fluent answers about the wrong company
A model that has never been given your contracts, tickets and records will write confidently about a business that is not yours. Grounding is the difference between plausible and correct.
- 02
Assistants that can only talk
An assistant with no permission to act inside your systems of record leaves the work exactly where it was. Every useful outcome ends with someone copying text into another tab.
- 03
No way to tell when it got worse
Prompt edits, model upgrades and new data all move quality in both directions. Without graded cases you hear about a regression from a complaint rather than from a test run.
The Approach
Build the harness before the agent.
We start from the task, the systems it touches and how you will judge the output. The model is picked last, once there is something to measure it with.
- 01
Grounded in your own data
Documents, records and policies indexed with the access rules they already carry, so every answer can be traced back to the passage it came from and to a reader allowed to see it.
- 02
Narrow tools, real permissions
Actions are exposed as small typed tools against your systems, each with its own scope and audit trail, rather than one credential that can do anything the model imagines.
- 03
Evaluation your team can run
A graded set of real cases kept in your repository and wired into CI, so the check outlives the engagement and the next change is scored rather than argued about.
Capabilities
Where agents earn their place.
The list below is not a product catalogue. Each shape is assembled from the same parts — retrieval, tools, evaluation, escalation — arranged for the job in front of it.
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Retrieval over private data
Question answering across contracts, policies, tickets and internal documentation, including the reply that nothing on file covers the question — the answer most systems will not give.
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Internal copilots
Assistants embedded in the helpdesk, CRM or admin panel your team already lives in, so the suggestion arrives inside the task instead of in a separate window nobody opens twice.
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Agentic workflows
Multi-step tasks where the model plans, calls tools, checks its own output and stops to ask when confidence is low, with every step recorded in a trace you can replay.
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Document and intake processing
Forms, invoices, applications and correspondence turned into structured records validated against a schema, with anything ambiguous handed to a person rather than quietly guessed.
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Triage and routing
Inbound tickets, emails and enquiries sorted, prioritised and assigned with the reasoning attached, so the queue arrives ordered and a supervisor can see why any one item landed where it did.
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Guardrails and escalation
Confidence thresholds, output validation, refusal paths, and a queue where a person confirms the decisions that should never be fully automatic, with a record of who approved what.
Technologies
What we tend to reach for.
Picked so the model layer can be replaced without rewriting what surrounds it. The rest is ordinary infrastructure, chosen because your team can run it after we have gone.
Models and providers
Orchestration
Retrieval and data
Evaluation and operations
How we work
From candidate task to daily use.
- Step 01
Pick the task, not the technology
We look for work with a clear input, a checkable output and a person who does it today. If nothing on your list qualifies, we say so before anyone writes a prompt.
- Step 02
Agree what correct looks like
Real cases with agreed answers, collected alongside the people who do the job. This becomes the scoreboard everything later is measured against, including our own suggestions.
- Step 03
Run it against real records
A thin path, end to end, against live data rather than curated samples, so the awkward cases surface while there is still time to design around them.
- Step 04
Tune one variable at a time
Prompting, chunking, retrieval strategy, model choice and tool design are changed separately and scored, so an improvement can be attributed instead of assumed.
- Step 05
Ship narrow, then widen
Release on a limited scope with thresholds and an escalation path, read back the cases it handled badly, and extend the scope only as the scores allow.
Bring us a task worth automating.
Describe the job, who does it today and how you would know it had been done right. We will say plainly whether an agent is the right instrument, or whether something simpler is.