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.
A chatbot answers. An agent finishes the job.
A chatbot answers. An agent finishes the job.
A chatbot answers. An agent finishes the job.
The difference is what happens after the answer. A chatbot returns text and leaves the work with the person who asked. An agent works out what is actually needed, grounds the answer in your documents and data, and then does the thing — in your systems, under your rules.
Interprets what the person actually needs, across channels, phrasing and language — not a menu of pre-set intents.
Grounds every answer in your documents, policies and data, with the source retrievable. Where the material does not answer the question, it says so.
Updates the CRM record, raises the ticket, checks the order, books the slot — or escalates to a person with the context already attached.
The problems this is usually bought to solve
Questions arrive when nobody is on shift. The agent answers in full at any hour and escalates what it should not decide.
Routine lookups and status questions absorb a support or HR team's day. These are the cases with the clearest payback.
Policies, specifications and procedures spread across systems, so the answer exists but the person asking cannot reach it.
Customers and inbound leads wait in a queue while the enquiry is triaged by hand, and some of them leave.
Enquiries reach sales unscored. The agent qualifies against your criteria first, so a person spends time on the ones that matter.
Customers, employees, prospects and field teams — one agent layer with different permissions and knowledge behind each.
An agent that cannot reach your systems can only describe the work. We integrate with CRM, helpdesk, ERP, order systems, HRIS and LMS platforms, so it reads the real state of a record and writes back to it.
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.