AI AssistantStart a project

An assistant that only knows what is on your marketing site is a deflection machine, and people learn to skip it within a week. It becomes useful the moment it can read your actual data and take an actual action — check an order, issue a refund, book a slot, update a record.

Grounding is the whole job

Every factual answer is retrieved from a source you control and cited back to it. We build that retrieval layer deliberately: chunking tuned to the shape of your documents, hybrid keyword-and-vector search, and a reranking step before anything reaches the model. When retrieval finds nothing relevant, the assistant says so and hands over. That behaviour is enforced by tests, not hoped for in a prompt.

Actions, not just answers

Reading is half the value. We connect the assistant to the systems where work actually happens — your CRM, your booking calendar, your billing — with an explicit allow-list of what it may do unattended and what needs a person to confirm. Every action is logged with the reasoning that led to it.

Tested before anyone sees it

Before launch we assemble an evaluation set from your real conversation history: typically a few hundred cases covering the common paths and the nasty edges. Every change runs against that set, so you can see whether a tweak actually improved anything. We also run an adversarial pass — prompt injection through user input, attempts to extract another customer's data, attempts to make the assistant promise something the business will not honour.

Kept honest after launch

Assistants drift as products, policies and data change. We set up the loop that catches it: conversation sampling, clustering of escalation reasons, and a weekly review of the cases it got wrong. Most of the value in month six comes from that loop rather than from the original build.