Chatbots & AI Assistants

What this looks like in practice

We start in your transcripts, not in a prompt. Months of real conversations, read and categorised, because what customers actually ask is reliably different from what the FAQ says they ask — and the gap between those two is where a bad assistant makes its worst mistakes.

That reading produces a boring, useful artefact: a list of intents ranked by volume, each marked as something the agent can finish, something it can start and hand over, or something it should not touch. Most of the value sits in the first category and most of the risk sits in the third.

Scoped to what it can finish

An assistant that resolves the top six questions completely is worth more than one that attempts everything and half-finishes it. Where it cannot finish, it should say so early and pass the conversation on with the context already gathered, so the customer does not repeat themselves.

Escalation is one step

No maze. No loop back to the main menu. The route to a person is a single move, and it is always available — including on voice, where being trapped is the complaint that generates the angriest calls.

Tuned against transcripts, weekly

After launch we read what it actually did. Wrong answers become test cases, near-misses become prompt changes, and the intents that turned out to be more common than the historic data suggested get promoted. This is the part that turns a launch into a system that keeps working.