Customer Experience
AI your customers talk to, and copilots for the people who answer them, built over knowledge you own and checked whenever a model changes.
Customer-facing AI is the most exposed software a company runs. It answers in your name, to people who did not choose to talk to a model, about things that cost money when they are wrong. We build these systems the way that exposure demands: grounded in knowledge you own, bounded to what the system is allowed to say, and watched by the people already accountable for the answer.
Copilots, assisted replies and self-serve answers
Most of this work takes one of three shapes. A copilot beside the person handling the case, retrieving policy and account context and drafting a reply they can edit. Assisted response, where the system writes the answer and a human approves it before it leaves the building. And self-serve answering, where a customer asks in their own words and gets a grounded answer with its sources attached. Which shape fits depends on the risk carried by the conversation, and most service organizations end up running more than one.
Escalation is scope, not a fallback
The failure everyone has experienced is the system that insists it can help while the customer is asking for a person. We design the exit before we design the answer. Thresholds are written down: which cases the system resolves, which it drafts for review, which go straight to a human, and what evidence triggers each. When a conversation moves to a person, the transcript, the retrieved sources and the system's own uncertainty move with it, so the customer does not start again. Your risk and service leaders sign that design before we build against it.
Grounded in knowledge you own
A customer-facing system is only as truthful as the material behind it, and in most companies that material is scattered across a help center, a policy library, ticket history and the heads of the longest-serving people. We build retrieval over the sources you own and control, with an explicit pipeline for how content enters, gets updated and gets retired. Contradictions and stale pages surface as work for your content owners rather than as confident wrong answers. Every answer carries its sources, so a reviewer can check a claim instead of trusting it.
The evaluation suite that keeps answers accurate
Models change underneath you, prompts get edited, and policy content moves. Without a test, none of that is visible until a customer finds it. We build an evaluation suite from your real questions and your own graded answers, covering what the system should refuse as carefully as what it should resolve. It runs before any model, prompt or content change ships, the same way code ships behind tests. The suite is a deliverable you own, and it is the clearest evidence your risk function will get that the system still behaves.
What your service organization is left with
Quality review changes shape. Instead of sampling a handful of contacts, your leads can read what the system actually said, with the sources it used and the point at which it handed over. We wire that logging into the tooling you already run, and we work through it with the people who own service quality until the review habit belongs to them. Runbooks, prompt and content ownership, and the evaluation suite transfer with the system, so changing it later does not require us.
What you get
- A service copilot that retrieves the relevant policy, drafts the reply and shows the source behind every claim it makes.
- An escalation design in writing: which cases the system resolves, which it drafts for a human, and which it never answers alone.
- Retrieval over knowledge you own, with a documented pipeline for how content enters, gets updated and gets retired.
- An evaluation suite built from your real questions and your own graded answers, run against every model, prompt and content change.
- Answer logs and observability wired into the tooling you already run, so quality review reads what the system said rather than sampling around it.
- Runbooks and working sessions with your service and engineering leads, so the system can be changed without us in the room.
Evidence
Perdais is a visa platform we built and still run, where the candidate outside the firm and the attorneys and case staff answering them work from one file, and what each side is allowed to see is designed rather than assumed.
Perdais and the rest of what we have built →
What this is not
This is not contact center outsourcing and it is not a licensed chatbot product. We do not take over your service floor as an outsourced operation and we do not resell someone else's platform under our name. Where people work alongside the system, they supervise and improve it rather than replacing your team on the queue. We also do not quote containment or deflection figures before anyone has looked at your knowledge and your traffic, because those numbers are a property of your content and your customers rather than of a sales conversation.
Talk to a partner
If you already know which conversations you would never let a model handle alone, you have the beginning of a scope worth talking through.
