AI Agents & Integration
Agents that do a specific job inside your existing stack — scoped narrowly enough to actually work.
What this actually is
The gap between an impressive demo and something you can put in front of customers is mostly plumbing: where the context comes from, what happens when the model is unsure, and who gets told when it goes wrong.
We build for that gap. Every agent we ship has a defined job, a defined set of data it can see, and a defined escalation path to a human. It logs what it did, so when someone asks why it replied that way, there is an answer.
That deliberately rules out the vague company-wide assistant. A narrow agent that reliably handles one workflow earns its cost immediately; a broad one usually gets switched off by month three.
What changes for you
- A repetitive workflow handled end-to-end without a person in the loop
- Answers grounded in your documents rather than the model's guesswork
- Clean handoff to a human the moment confidence drops
- A log of every action taken, reviewable after the fact
What we build it with
Models
Orchestration & retrieval
Integration
What's included
Custom AI agents & copilots
LLM integration into your tools
Document processing & RAG systems
Chatbots with human handoff
AI-powered workflow automation
From first call to handover
Pick the workflow
We look for something repetitive, well-defined and currently eating hours. Narrow beats ambitious — the first agent should be obviously useful, not impressive.
Ground it in your data
We wire up the documents, records and systems the agent is allowed to see, and set the boundaries of what it can act on.
Test against real cases
We run it on your actual history, including the awkward edge cases, and tune until the failure mode is 'asks a human' rather than 'invents an answer'.
Ship with a human escape hatch
It goes live with monitoring, logging and a clear handoff path, so the team can see what it is doing and step in whenever they want to.
Questions about ai agents & integration
How do you stop it making things up?
By grounding answers in your own documents rather than the model's memory, and by designing the uncertain case to escalate to a person instead of guessing. We tune against your real history until that behaviour is consistent.
Does our data get used to train a model?
No. We use the providers' business APIs, where inputs are not used for training, and we scope each agent to only the data it needs for its job.
What if the model we build on gets superseded?
The integration is built so the model is a swappable component. Moving to a newer one is a configuration change and a re-test, not a rebuild.
This service in the wild
Have a project in mind?