We build private AI assistants that answer questions based on approved company documents, procedures and internal materials instead of guessing or relying on the model's generic knowledge.

This is a fit for companies that want to shorten information search time, reduce repeated questions to experts and still keep control over data scope, access rules and the way answers are generated.

Discuss a knowledge assistant See dedicated applications

A company AI assistant built on a knowledge base and RAG

Usually when the knowledge already exists, but it is spread across documents, procedures, PDFs, notes, repositories and the people who answer the same questions again and again.

  • employees keep asking about the same procedures, rules and statuses,
  • sales, service or back office teams search for answers in many places,
  • onboarding new people takes too long,
  • answers should be based on approved sources rather than improvisation,
  • the company wants to reduce the risk of exposing data in public AI tools.

How does it work in practice?

  • 1. Source selection - we define which documents, databases and materials may feed the assistant.
  • 2. Content organization - we prepare structure, metadata, permissions and update rules for the knowledge base.
  • 3. Retrieval layer - the assistant first finds relevant fragments and only then builds an answer.
  • 4. Citations and control - the answer can point to the document, section or source it used.
  • 5. Access restrictions - the visible knowledge scope may depend on role, team, customer or process.

How is this different from a basic PDF chat?

Persistent knowledge base

This is not a one-off upload of a few files. We build a structured mechanism that can be updated and extended with the process.

Source control

The assistant works on approved company materials, with a defined scope of documents, permissions and versions.

Process integration

The solution can be embedded in a panel, intranet, CRM, form or business application used by the team.

Lower hallucination risk

Answers are built from retrieved knowledge fragments rather than the model memory alone.

Local model, isolated cloud or a hybrid architecture?

Local deployment

For organizations that want to minimize data exposure and keep processing inside their own infrastructure.

Cloud deployment

For teams that want a faster launch while keeping control over data scope and access policy.

Hybrid deployment

For companies that want to combine private data, local repositories and selected external services in one flow.

How do you control sources, access and answer freshness?

  • we can work on a separated document set instead of the entire company repository,
  • for selected processes, pseudonymization or anonymization can be applied before analysis,
  • permissions may limit document and answer visibility to a specific role,
  • the answer logic can distinguish allowed, restricted and human-escalation questions.

Example use cases

  • an internal procedures and instructions assistant for the team,
  • answer support for sales, service or customer support teams,
  • an assistant for project, technical or proposal documentation,
  • support for onboarding new employees,
  • searching answers across contracts, policies, checklists and standards,
  • a knowledge module embedded in a dedicated process application.
Starting at PLN 9,900 net

The starting version may answer questions based on an agreed document set and point to the sources.

The exact scope, timeline and price are agreed after the conversation.

Discuss the scope

What will this assistant not solve on its own?

  • it will not replace missing document ownership or poorly organized knowledge,
  • it should not answer beyond the scope of the approved knowledge base,
  • it does not remove the need to review answers in high-risk processes,
  • it creates little value if the company does not define which sources are official and current.

How to start?

The safest start is one area: one process, one document group or one team. That makes it easier to test answer quality, citation style, access limits and the real operational value before a broader rollout.

Describe the document scope and questions ➜

Related areas

A good first scope

One team, one document type and one group of questions is enough to evaluate whether a private assistant creates real value.

Possible embedding

The assistant may work as a standalone panel, an application module, a form helper or a knowledge layer inside an operational process.

Discussion topic

The contact form opened from this page already sets the scope to private company knowledge assistant.

Discuss a knowledge assistant