AI agents and copilots
A general chatbot knows everything about the world and nothing about your business. The useful version is narrower and far more valuable: an assistant connected to your systems, permitted to do a specific set of things, and accountable for getting them right.
Demos are easy. The last mile is the work.
Wiring a model to a chat box takes an afternoon. What takes real engineering is everything after: giving it safe access to live systems, keeping it inside its remit, handling the cases where it is unsure, logging what it did so somebody can audit it, and knowing when it has degraded. Skip that and you ship something confident and wrong.
You’ll recognise this if
- Your team answers the same questions repeatedly from scattered sources
- A chatbot pilot answered fluently but got specifics wrong
- Staff are pasting company data into consumer AI tools
- Work stalls because information lives in someone's head or inbox
What we actually deliver
An agent scoped to actual tasks
We start from the jobs it should do — draft this reply, look up this order, summarise this account, file this ticket — rather than from a chat window with unlimited scope. Narrow assistants are the ones that get used a year later.
Real connections to your systems
Wired into the tools you already run, respecting the permissions those systems already enforce. A user should never see through the assistant what they could not see by logging in directly.
Guardrails and a defined refusal
Explicit boundaries on what it will attempt, and a designed path for the cases it should hand to a human. An assistant that says it doesn't know is worth far more than one that always answers.
Evaluation and logging from day one
A test set drawn from your real questions, run on every change, so you can see whether an update made it better or worse. Every action logged, so an answer can always be traced back and explained.
The way we approach it
One task, all the way through
We pick the highest-value task and take it fully to production — permissions, evaluation, monitoring, the lot — before adding a second. A narrow assistant that genuinely works beats a broad one nobody trusts.
Human in the loop where it matters
Anything that sends, spends, or changes a record starts behind human confirmation. Where the record shows it earning trust, we can relax that deliberately — as a decision, never a default.
Built to survive the model changing
Model providers deprecate and reprice. We keep the model boundary clean so you can move to a different one without a rewrite, and so a version change can be tested against your evaluation set before it goes live.
What changes
- An assistant your team actually uses after the novelty passes
- Answers traceable to a source, and actions traceable to a log
- A measurable quality bar, so changes can be judged rather than guessed at
- Company data kept inside systems you control
Asked often enough to answer here
Will our data be used to train someone's model?
Not under the arrangements we set up. We use enterprise API terms where provider retention is off by default, and we make where your data goes an explicit, documented part of the design rather than an assumption.
Which model do you use?
Whichever fits the task, the budget, and your compliance position — and we keep the boundary clean so it can change. That decision is made with evidence from your own evaluation set, not from a benchmark chart.
What happens when it gets something wrong?
It will, occasionally — that is why evaluation, logging, and human review are in scope from the start rather than added after an incident. The design question is never whether it errs, but whether an error is caught, visible, and recoverable.
Related
Wherever you’re starting from, let’s figure out the next step.
Tell us what you’re building — we’ll tell you honestly whether we’re the right team for it.