AI Chatbots
Chat support trained on your knowledge base, live instantly.
The old chatbots were decision trees wearing a friendly face. They frustrated people because they could only handle the questions someone had anticipated, and everything else hit "I didn't understand that."
The current generation is different, but it introduces its own risk: a model that will confidently answer a question about your pricing that nobody ever told it.
The engineering problem is grounding — making sure the agent answers from your actual documentation and says "let me get someone" when it doesn't have the answer. That constraint is most of the work, and it's the part that separates a useful chat agent from a liability on your homepage.
What we build
Grounded knowledge agents
Trained on your documentation, pricing, policies and past support conversations. Answers come from your material, not from a general model's assumptions.
Lead qualification in chat
The agent identifies who's worth talking to, captures the details, and books the meeting without a form.
Honest escalation
Clear boundaries, a clean handoff to a human with the conversation summarised, and no invention at the edges.
CRM write-back
Every conversation logged against the contact, so your team opens a chat already knowing the history.
Multilingual support
Answering in whichever language the visitor opens with.
Continuous improvement
Review of real conversations, with gaps fed back into the knowledge base rather than left to recur.
How it gets built
System Diagnosis
Your existing support volume and the questions that actually come in, ranked by frequency.
Fracture Analysis
What's being answered repeatedly by a human, what's going unanswered, where response time is costing conversions.
Architectural Blueprint
Knowledge sources, escalation rules, tone, and the handoff design. Approved before build.
Engineering & Launch
Built, tested against your real historical questions, deployed to a subset of traffic first.
Track and Manage
Weekly conversation review for the first month. This is where a chat agent goes from adequate to good.
Scale & Dominate
More channels, more languages, deeper integrations.
What we won't do
We won't deploy an agent that guesses. If your documentation doesn't cover something, the agent will say so and hand over — even though that produces a lower "resolution rate" number. A confidently wrong answer about your pricing or your policy costs more than an honest handoff ever will.
Questions, answered.
How is this different from the chatbot we tried before?
Older chatbots followed scripted decision trees and broke on anything unanticipated. These read your actual documentation and answer from it. The failure mode is different too — the old one couldn't answer, this one has to be engineered not to over-answer.
Will it make things up?
That's the specific risk we build against. The agent answers from your source material and is constrained to escalate rather than improvise. We test this adversarially before launch.
Can it hand over to a human?
Yes, with the full conversation summarised so your team isn't asking the customer to repeat themselves.
What does it need from us?
Whatever documentation you already have — help articles, policies, pricing, past support threads. If that material is thin, we'll tell you during diagnosis, since the agent can only be as good as what it's grounded in.
Send us your top twenty support questions. We'll show you how many an agent could handle on its own.
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