Overview
An agent acts only through the tools we give it, with the permissions we agree. Every step is logged. A person approves anything that cannot be undone, and can take over at any point.
Each assistant or agent has an evaluation set built from your real cases. It ships when it passes, and the same set runs on every change after launch.
Who it is for
- Service and operations teams handling a high volume of repeat questions or tasks.
- Knowledge-heavy teams whose answers live across documents, wikis and tickets.
- Engineering teams that want copilots tied to their own code and runbooks.
- Back offices with multi-step work across several systems.
What you get
- Knowledge assistants over your documents and policies, with sources shown.
- Copilots for service, sales, engineering and operations teams.
- Agents that complete multi-step tasks across your tools.
- Tool and API integrations with the systems you run.
- Permissions, approval steps and a full log of what the agent did.
- An evaluation set and a dashboard for quality, cost and escalations.
How it runs
- Pick one task with clear volume and a clear definition of done.
- Collect real examples and build the evaluation set.
- Build the assistant or agent with the smallest set of tools it needs.
- Shadow mode: it suggests, a person decides, and we measure.
- Supervised go-live, then wider autonomy only where the numbers support it.
In practice
- A finance-operations assistant that answers supplier and payment queries from the ERP.
- A service agent that checks an order, issues a refund under a limit and escalates above it.
- An engineering copilot that answers from runbooks and opens tickets with the right context.
- A finance agent that matches invoices to orders and queues exceptions for a person.
Technology
- Models from Anthropic, OpenAI and Google, and open models where they fit.
- LangChain, LangGraph and the Model Context Protocol for tools.
- Vector search over your content, with access rules carried through.
- Tracing and evaluation with LangSmith, Langfuse or your own logging.
How we charge
A fixed-scope build for the first assistant or agent, then a monthly run that covers supervision, evaluation and model costs reporting.
Questions
Can an agent make a mistake on its own?
Only within the limits we set. Anything costly or irreversible needs a person’s approval, and every action is logged.
Does it respect who can see what?
Yes. Retrieval carries your existing access rules, so a user only gets answers from documents they are allowed to read.
How do we know it is working?
The evaluation set runs on every change, and a dashboard shows resolution, escalation and cost per task.
Talk to the team
Two-week, fixed-fee discovery. Production release by week 12.
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Let’s talk about what you want to ship.
Start with a two-week, fixed-fee discovery. You get a ranked portfolio, an architecture, a cost model and a fixed-price build plan.