01
Choose the use case
We identify who will use the knowledge base and which questions it should answer. We consider how often questions arise, time spent finding information and situations that require a human decision.
RAG and company knowledge
RAG knowledge bases
We design a company knowledge base to help find answers in selected documents. Source references and access are configured to agreed permissions. We test integrations and answer quality using company material; important results require human review.
Trusted by
Companies and institutions that need stable IT.
Ask a question, find company knowledge
Do employees search through folders, ask about the same procedure or use an outdated proposal? RAG combines an AI model with search across selected company documents. A user asks a question; the system retrieves relevant passages and prepares an answer with source references. It can support sales, helpdesk and teams working with documentation. It does not replace the person responsible for keeping information current or the need to check an important decision.
Tools for your company knowledge base
We select components to fit your existing environment. The services below can be combined in one implementation; not every project needs every tool.
Offline AI models
Run a model in your company's environment. We compare hardware needs, costs and answer quality with cloud or hybrid options.Open WebUI
An interface for conversations with a model and access to documents. We configure accounts, user groups and access to selected knowledge collections.Paperless AI
Organise documents and scans before adding them to the knowledge base. Optical character recognition, or OCR, and metadata help make the right material searchable.n8n automation
Update documents and connect the knowledge base to workflows. Answers can support message drafting or ticket handling with agreed checks by an employee.ISO 27001 · TÜV NORD Polska
TÜV NORD Polska has certified Virtline's information security management system to PN-EN ISO/IEC 27001:2023-08. This is our company's certificate, not a guarantee of error-free AI answers.
View Virtline's certificateRAG implementation process
We start with a limited trial for one team or area of knowledge. The schedule depends on document quality, integrations and data requirements.
02
Prepare the sources
We review selected material from SharePoint, Confluence, file servers or other agreed systems. We remove duplicates, identify versions and set metadata and access rules. Scanned text needs a quality check.
03
Configure and test
We select the model and retrieval approach, including the vector database and result ranking. We test answers in Polish and other languages used in your business. We check source references and permissions using test accounts. Users assess answers to real questions.
04
Handover and development
We compare answer quality and search time with the criteria agreed before the pilot. We identify errors in documents or configuration that need correcting before launch. Together, we assess expected savings and running costs to decide whether to proceed with deployment. We agree updates, monitoring and responsibility for support.
Practical scope
What the implementation includes
We agree the scope before starting. At handover, we check how the solution works, its limitations and the materials needed to operate it.
A list of approved documents, versions and people responsible for keeping them current. Defined user groups, document permissions, retention periods and rules for excluding confidential or outdated material.
Prepared documents, search and an answer interface with source references. Configuration of the model, text segmentation and search index, with the integrations specified in the project scope.
A set of user questions, checked sources and a record of errors found. Tests cover access, missing or conflicting documents, response times and system behaviour when there is no basis for an answer.
Configuration details and instructions for updating sources, backups and rollback. Rules for usage logging, reporting errors and repeating tests after changes to documents, permissions or the model. Ongoing support is agreed separately.
When to organise the documents first
If procedures contradict each other or nobody confirms that they are current, installing AI will not solve the problem. We first identify reliable sources and responsible people. A simpler search tool may be enough for a small collection of files. For confidential data, we define what may leave the company environment, who can access logs and how long they are retained. A local setup still needs security controls, updates and answer checks.
Questions about company knowledge bases
Is an answer with a source reference always correct?
+
No. A model can misread context or combine information incorrectly. We therefore test answers with users, provide source references and identify situations that need a human check. If there is no basis for an answer, the system should say so; we also test that behaviour.
Which documents can be used?
+
Examples include instructions, procedures, contracts, proposals, technical documentation and approved service tickets. We check formats, languages, currency and permissions. PDFs and scans may need preparation; we confirm whether a specific system can be integrated before quoting.
Does the data have to go to a public AI service?
+
No. The model and search can be planned within your company environment, or as a hybrid setup. Before selecting an option, we check data flows, administrative access, logs, hardware requirements and costs. Calling a setup offline does not replace configuration and security checks.
How long does implementation take, and who updates the knowledge base?
+
Timing depends on the number of sources, document quality, integrations and access rules. After reviewing them, we agree a trial schedule. Your company appoints people to confirm that content is current; technical updates and ongoing support are defined in the agreed scope.
Let's discuss knowledge in your company
Tell us where you keep documents, who uses them and a few typical questions. We will discuss a trial scope and how to evaluate its usefulness. Please do not send confidential files in your first message.
Discuss your knowledge base