Your business knows.
Help your people
find the answer.

A knowledge base chatbot answers questions using a selected collection of documents or records. It retrieves relevant material and uses it to prepare an answer, ideally with references the reader can inspect. A business implementation also needs access controls, a content-update process and evaluation of whether the answer is actually supported.

RAG stands for retrieval-augmented generation. A RAG chatbot retrieves relevant passages from a knowledge collection and supplies them to a language model as context for a response. This lets the application use selected business information without relying only on the model’s general knowledge. Retrieval and generation can both make mistakes, so the system still needs evaluation and a way to handle insufficient evidence.

Not necessarily. We first assess whether your existing tool can answer representative questions, respect permissions and keep up with content changes. Configuration or better source material may be enough. Custom development becomes useful when you need a particular portal experience, connections across systems or customer-specific access that the existing product cannot reasonably support.

No. Start with a representative sample from the team or process you want to improve. We review versions, structure, formats and missing context, then include the necessary preparation in the scope. Your subject experts decide which instructions are authoritative; Anteomnio handles the agreed document preparation and technical implementation. Scans, complex tables and image-only instructions may need additional work.

No. A source can be relevant without supporting every claim in an answer. We test whether each response is supported by the retrieved passages, whether the references point to the right material and whether the system recognises missing evidence. Important decisions still need the appropriate human review.

We design the assistant to explain the gap, ask a useful clarification or direct the question to a named owner. When documents conflict, an agreed version or authority rule may resolve the difference; otherwise the conflict should be surfaced. The assistant should not silently combine incompatible instructions into a new policy.

Yes, when the source and application permissions support the required design. We define who can retrieve each document and enforce access before material is passed to the model. References and source links must respect the same rules. Permission changes and revoked access are included in the update and evaluation process.

We agree how additions, edits, deletions and permission changes reach the assistant. The implementation may use a supported connector, a scheduled refresh or an approved publishing workflow. Content needs a responsible owner, a clear source of truth and a way to confirm that an update has actually reached retrieval. Refresh frequency is part of the scope.

We assess the authentication, interface and integration options available in your application. The assistant can be scoped for an internal workspace, a customer portal or a public help experience. Each audience needs its own approved collection and permissions; internal documents should not become public simply because the interface is shared.

Implementation depends on the source systems, document preparation, access rules, interface, integrations and evaluation work. Running costs can include retrieval infrastructure, model usage, storage, refresh jobs, monitoring and maintenance. We separate implementation from recurring estimates and state assumptions about collection size, question volume and content changes.

The agreed release includes the assistant interface, source connections, access controls and publishing process defined in the scope. Handover covers the agreed source code, configuration, account access and operating notes, together with evaluation findings and known limitations. We also identify who maintains the content and which technical support or maintenance is included, so responsibilities are clear after launch.