Enterprise knowledge assistants & secure RAG

Choose the knowledge problem worth solving

Teams often lose time finding the right policy, comparing document versions or asking colleagues the same operational questions. We select a defined audience and a bounded collection of approved information before building an assistant. The discovery stage records the questions people actually ask, how they answer them today, the cost of a wrong response and which decisions require a qualified human.

Prepare sources and preserve their boundaries

A useful assistant depends on document quality, ownership and permissions. We inventory repositories, identify authoritative versions and define update and deletion workflows. The retrieval design preserves relevant document context and carries access restrictions through ingestion, indexing, retrieval and presentation. An employee should not gain access to restricted information simply because a search or generated answer passes through an AI system.

Ground answers in relevant evidence

Retrieval-augmented generation, or RAG, supplies selected source material to the model when a question is answered. We design retrieval, answer structure and citations around the business task. The system should distinguish an answer supported by approved sources from a request with insufficient information. Citations are checked for whether they actually support the answer; a plausible-looking reference is not enough.

Evaluate Greek, English and difficult questions

We build a test set from real questions, including ambiguous wording, abbreviations, outdated instructions, conflicting sources and questions the assistant should decline or escalate. Greek morphology, mixed-language documents and organisation-specific terminology are evaluated directly. Measures can include retrieval relevance, answer correctness, unsupported statements, permission failures, response time and the amount of human correction required.

Integrate the assistant into a maintainable workflow

The delivery can include an internal portal, service-desk integration or another approved work environment. We define identity, logging, feedback and content-owner responsibilities, alongside provider settings and data handling. A phased rollout lets a small team validate usefulness before wider access. Changes to models, retrieval or source collections pass through regression checks, with a documented fallback when the service cannot provide a reliable answer.

Find policies, technical knowledge and approved answers

Useful starting points include an internal policy assistant, a support knowledge search or a technical reference tool for a distributed team. We identify the questions that currently require repeated searching or expert interruption, then select the approved source collection. The assistant should help users locate the relevant material and understand its context, with a path to a responsible person when the source does not resolve the question.

Handle conflicting, obsolete and restricted documents

A document repository can contain several versions of the same policy or material intended for different entities. We define how source ownership, effective versions and access rules influence retrieval. Tests include questions whose answers changed and users who should not see a document. The content owner needs a practical way to update or withdraw sources and confirm that the assistant reflects the change.

Measure answer usefulness beyond fluent language

An answer can sound clear while relying on the wrong source. Evaluation should examine whether the retrieved material is relevant, whether the answer is supported and whether the user can follow the reference. We include unanswered and ambiguous questions as well as routine ones. A pilot review compares search effort, reviewer corrections and the remaining escalation burden with the previous knowledge workflow.

Common questions