Knowledge systems that answer
and cite their sources.

Retrieval-augmented generation keeps your knowledge in your documents and teaches the system to look things up. A query retrieves the relevant passages, and the model answers only from what was retrieved, citing it. Fine-tuning (baking your data into the model's weights) is the wrong tool for facts: it's expensive to update, impossible to cite, can't respect per-user permissions, and confidently blends what it half-remembers. With retrieval, updating the system means updating a document; removing access means removing a document from someone's index. Where fine-tuning tone or format earns its keep, the discovery document will say so.

A chatbot is a surface; a knowledge system is the machinery underneath. Our chatbot service ships a conversation, typically customer-facing, on a support or sales queue, with escalation rules and helpdesk integration. This service builds the retrieval layer itself, and chat is only one of the places it shows up. The same pipeline can power site search that answers instead of listing links, a lookup panel inside your agents' helpdesk, and internal assistants for HR, finance or engineering questions. If what you need is a support chatbot, start there. It stands on this layer anyway. If several teams keep asking questions of the same sprawling documentation, start here and add surfaces as they earn their keep.

Any system with a language model in it can, and no vendor can promise zero. What a well-built knowledge system changes is the failure mode: answers are generated only from retrieved passages of your own documentation, every answer carries its citations so a human can check it in seconds, and when retrieval comes back empty or the sources disagree, the system says exactly that instead of guessing. Before launch it runs against an evaluation set built from your team's real questions (grounding and refusal cases included), and you see the scores before sign-off. After launch, every query and answer is logged. Honest answer: not zero errors, but rare, visible, checkable ones.

Access control is designed into retrieval, not bolted onto the interface. The pipeline inherits your existing model (SSO groups, drive and folder permissions, workspace boundaries), so a query only ever searches the documents that user could already open. Confidential material isn't filtered out of an answer at the last moment; it was never retrieved. Every query is logged and auditable, work runs under NDA as standard, and where you need it the whole system deploys inside your own cloud so nothing leaves your infrastructure. We also use enterprise API terms under which providers don't train on your data. Retention, residency and access rules are agreed in the scope document before anything is indexed.

Usually yes, and the audit will say where not. Messy-but-real documentation (dated policies, overlapping wikis, tickets that contradict the manual) is the normal starting point. Part of the build is triage: which sources are authoritative, which are stale, and which contradict each other (the system flags these rather than averaging them). What retrieval cannot fix is knowledge that was never written down. Where the audit finds those gaps, you get a prioritised list of the questions people actually ask that no document answers, which tends to be the most useful documentation roadmap a team has ever received. We'd rather scope a smaller set of documents that answers well than a sprawling one that answers plausibly.

It depends on the documentation, the permission model and the surfaces served, which is why we don't quote from a rate card. Every engagement opens with a scoped pilot: one body of documentation, one surface, your own questions as the evaluation set, working within 14 days and priced as a small, defined build. The proposal you receive within 48 hours itemises what ships, including sources indexed, permission rules enforced, evaluation included, and what handover looks like. Extending to more sources or surfaces is priced per step, only after the pilot has proved itself on your own questions.

Something we build over your own documents and hand over. One retrieval pipeline serves site search, a lookup inside your helpdesk and internal assistants, with your existing permissions respected. At handover your team gets the runbook and outright ownership of the code, prompts and pipelines.