An agent that answers your customers, not a chatbot that deflects them.
We build agents that hold a real conversation, look things up in your systems, and complete the task — checking an order, raising a ticket, explaining a policy, qualifying a lead. Trained on your knowledge, not the open internet.
Running on private models we host ourselves. Your customer conversations never reach a public LLM.
- Reads your product knowledge and answers from it, with the source.
- Calls your systems — order status, account state, availability.
- Takes the action, not just the question: raises, updates, books.
- Hands to a human with the context already attached.
- Says it does not know, instead of inventing an answer.
Six places an agent earns its cost
First-line questions answered in full, at any hour, with escalation that carries the history with it.
HR, IT and policy questions your own staff ask repeatedly, answered from the current document rather than last year's PDF.
Inbound enquiries scoped and scored before a person spends time on them.
Contracts, specifications and procedures made askable, with the passage it came from shown.
Walking a customer or a new employee through a process step by step, adapting to what they answer.
The agent on this website is ours, built the same way. Ask it something difficult — that is the point of it being here.
The hard part is not the conversation
Any model can produce fluent text. What separates a working agent from a demo is grounding, integration, guardrails and knowing when to stop — and being honest about what it cannot do.
We scope narrowly, measure containment and accuracy on your real traffic, and expand only what holds up.
- 01Grounded in your knowledge
Answers come from your documents and systems, with the source retrievable.
- 02Connected to what matters
CRM, ticketing, ERP, order systems — so the agent can act, not only describe.
- 03Guardrails and escalation
Defined boundaries, refusal behaviour, and a handoff that arrives with context.
- 04Measured, then expanded
Containment rate, accuracy and escalation quality reviewed on real conversations.
- 05Private by default
Open-weight models we host, deployable in your own cloud. How we deploy privately →
One engagement, in detail
A support desk that answers in its own product's language
- Context
- A CRM company in Europe. Their support team answers detailed product questions, and the tickets contain their customers' customer data.
- The problem
- Volume was routine but the questions were specific — configuration, permissions, integrations. Generic chatbots deflected rather than answered, and the privacy position ruled out sending ticket content to a hosted model.
- What we built
- A conversational agent grounded in their own product documentation and resolved-ticket history, connected to the support system so it can look up account state and raise or update a ticket. Escalations arrive with the conversation and the attempted resolution attached.
- Deployment
- Entirely on privately hosted open-weight models, with full request logging for their compliance team. No conversation leaves the approved environment.
- Capabilities
- Knowledge groundingSystem integrationEscalation with contextPrivate deploymentAudit logging
Often part of a bigger change
An agent works best when the process behind it makes sense. If the workflow is the problem, start there instead.
Tell us the question your team answers fifty times a week.
A discovery session is a working conversation, not a demo. You will leave it knowing whether an agent is worth building — including if the answer is no.