What does it take to put an AI product in front of your customers safely?
Retrieval grounded in your own content so it cannot invent an answer, an escalation path designed before launch rather than after the first complaint, evaluation that runs on every change, and logging that lets you reconstruct any conversation. The model is the easy part. Everything around it is the engagement.
Customer-Facing AI Products
The model is the easy part
Every customer-facing AI project that has gone publicly wrong went wrong in the same places: it answered a question it had no basis for, it could not hand off to a person, nobody could reconstruct what it had said, and a model upgrade changed its behaviour without anyone noticing.
None of those are model problems. They are engineering and design problems, and they are where the work is.
What we build
Retrieval grounded in your own content, with citations, so an answer can be checked rather than trusted. When retrieval finds nothing relevant the assistant says so and escalates, which is a designed behaviour rather than a fallback.
Escalation designed before launch: what it declines, when it hands off, what context the human receives, and how the customer experiences the transition. Most teams design this after the first complaint.
An evaluation harness of real cases with known-good answers that runs on every change. This is the difference between a product you can maintain and one you are afraid to touch.
Conversation logging and review tooling, because you will need to explain a specific exchange, and the day you need to is not the day to build it.
Launch in stages
Internal first, then a limited cohort, then general, with a rollback at each gate and the evaluation suite green before each promotion. A launch that cannot be rolled back is not a launch, it is a bet.
What you get
- Grounded retrieval over your own content, with every answer citing its source
- Escalation design: what the assistant refuses, when it hands off, and what the human receives
- An evaluation harness of real cases with known-good answers, run on every model or prompt change
- Conversation logging and review tooling, so any exchange can be reconstructed and audited
- Guardrails for scope, tone, and the topics it must decline
- A staged launch: internal, then a limited cohort, then general, with a rollback at each gate
Questions we get
- How do you stop it making things up?
- Ground every answer in retrieved content from your own material and require a citation. If retrieval finds nothing relevant, the correct behaviour is to say so and escalate, and that path is built before launch. A model asked a question with no grounding will produce something plausible, which is the failure mode people mean when they say hallucination.
- What does the evaluation harness actually do?
- It holds a set of real questions with known-good answers and runs them on every prompt change, model upgrade, or content update. Without one, a vendor's model upgrade silently degrades your product and you find out from a customer. This is the artifact clients most often do not know to ask for and most regret not having.
- Who is accountable when it gets something wrong?
- You are, which is why the logging and the escalation design matter more than the model choice. We build so that any conversation can be reconstructed, the failure explained, and the fix tested before it ships.
How it starts
Three-week discovery, then eight to twelve weeks to a supervised launch. You keep everything produced, whether or not there is a next part.