Most chatbots are deflection machines — they exist so fewer people email support, and users can tell. We build the other kind: AI chatbots that actually resolve things, because they are grounded in your real documentation, connected to your real systems, and measured against your real definition of a correct answer.
What we build
Customer support assistants that answer from your help centre, policies and product data — with citations where it matters — and hand off to a human the moment confidence drops, with full context attached so the customer never repeats themselves.
Product assistants that live inside your app: onboarding guides, “how do I…” answers, settings navigation, data questions (“what did I spend on X?”) — the feature that makes your product feel like it has a manual nobody has to read.
Internal helpdesks for HR, IT and ops questions, grounded in the policy documents your team never reads until they need them.
Bots that act. Checking an order, rescheduling a delivery, filing the ticket — with explicit permissions and audit logs. When the conversation needs multi-step reasoning across systems, that is agent territory, and we build that too.
Why grounding is the whole game
A chatbot that answers from the model’s general training is a liability with your logo on it. Ours answer through retrieval over your content — the technique we explain here — so responses reflect what your business actually says and does. Update the document, and the bot’s next answer updates with it. No retraining, no stale answers from last year’s pricing page.
Measured, not vibes-checked
Before launch we build an evaluation set from your real queries — the common ones, the weird ones, the adversarial ones — and every change to the bot must pass it. You get a number for accuracy and hallucination rate, dashboards for resolution and escalation once live, and a feedback loop that turns bad answers into next week’s fixes. “It seems pretty good” is not a launch criterion.
The build
Weeks 1–2: knowledge audit and retrieval pipeline over your sources. Weeks 3–5: conversation design, guardrails, integrations, evaluation suite. Weeks 6–8: staged rollout — internal, then a traffic slice, then full — with escalation flows tested under real load. Budget-wise, see what a chatbot actually costs; ongoing inference for most support bots is modest and monitored from day one.
Want a bot your customers do not hate? Talk to us — we reply within one business day.