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Knowledge management
Make company knowledge from PDFs, emails, images, and policies searchable — and answer questions directly from approved sources.
Knowledge flow
The knowledge base connects existing sources with a controlled answer workflow. Each phase has a clear job.
Connect PDFs, emails, scans, and business systems in one place.
Extract text, apply OCR, and split content into useful sections.
Search by meaning rather than exact terms and provide context.
Generate answers with approved sources and matching permissions.
In daily work
Employees ask in their own words. The knowledge base brings the right information from approved sources into the workflow.
Traceable answers instead of guesses
Sources and permissions stay part of the answer — not just an invisible background process.
Knowledge assistant
You
Does the travel expense policy apply to domestic business trips?
Yes. The current travel expense policy applies to domestic business trips. Reimbursement follows the approved allowances and evidence requirements in the HR policy.
Sources
Digital documents, scans, emails, images, and business systems become searchable together — including metadata, sources, and permissions.
Scattered files become one shared knowledge layer — searchable, traceable, and matched to the work context.
RAG, embeddings, vector search, and LLMs work together. The four phases show what happens underneath — without blocking the reading flow.
First, relevant data sources are connected. Sources can be imported once or synchronized regularly.
Many documents are not immediately understandable for machines, so their content is technically prepared.
For digital PDFs, text is extracted directly. For scanned documents or images, OCR is used. OCR means Optical Character Recognition, which extracts text from images. Scanned contracts, letters, tables, and screenshots can then be analyzed.
Metadata such as filename, creation date, source, document type, or related person can also be stored.
Long documents are split into smaller sections called chunks.
This matters because an LLM should not always process a complete document at once. Instead, the system retrieves the most relevant text sections. Good chunking strategies keep related content together.
For example, a contract is not only stored as one file. It is divided into meaningful sections such as term, termination, payment conditions, and data protection.
Text sections are converted into embeddings. An embedding is a mathematical representation of the meaning of a text.
These embeddings are stored in a vector database. This allows the system to search by meaning, not only by exact terms.
If someone searches for 'How can a contract be ended?', the system can still find relevant passages that use terms like termination period, contract termination, or ordinary termination.
When a user asks a question, the question is analyzed semantically as well. The system then searches for the most relevant chunks in the knowledge base.
Filters can be applied so only fitting and approved information is used.
A role and permission model controls who may see which information.
RBAC stands for Role-Based Access Control. Permissions are assigned through roles instead of being assigned individually to every person. This is especially important when sensitive data is processed.
After relevant content has been found, it is passed to the LLM as context. The model creates an understandable answer from it.
The answer is not based only on general model knowledge, but on the company's approved documents.
Sources, document names, or passages can optionally be displayed so the answer remains traceable.
Depending on requirements, the solution can run fully self-hosted in an internal data center, on dedicated cloud infrastructure, in a European cloud environment, or as a hybrid setup with selected external AI services.
For especially sensitive data, a self-hosted LLM can be used. Documents, search queries, and answers then remain under internal control.
The important relationships remain available. Open only the area that matters for your decision right now.
In many companies, important information exists but is not easy to access. Documents are stored in network drives, SharePoint, Google Drive, email inboxes, scans, or business systems. Employees often need to know where information was stored, what a file is called, or which person knows the answer.
This creates lost time, duplicate work, and knowledge silos. It becomes especially difficult when information sits in scanned PDFs, images, email attachments, or old folder structures.
The solution is a central knowledge base that automatically processes company documents and makes them searchable. Content from PDFs, Office files, emails, images, and other sources is extracted, analyzed, and stored in a structured way.
Instead of searching for files manually, employees can ask questions in natural language. The AI searches the connected knowledge sources and returns an answer based on existing documents.
The special value is not only search, but controlled AI usage on the company's own data.
The solution can be built so sensitive data remains inside the company. Depending on requirements, components such as databases, document processing, and the LLM can be operated fully self-hosted. Confidential documents do not have to be transferred to external providers or abroad.
A role-based permission model can also control which people or departments may access which content. HR may only see HR-relevant documents, while technical teams can access technical documentation.
Large language models understand and answer language very well. They do not automatically know a company's internal documents. This is where RAG comes in.
RAG stands for Retrieval-Augmented Generation. The language model is not only asked a general question; it first receives relevant information from the company's own knowledge base.
This makes RAG a useful complement to classic LLMs. The model does not need to permanently memorize the knowledge, but can access current and internal information in a controlled way.
AI document analysis is especially useful for companies that regularly work with many documents and recurring information requests.
The main value is finding information faster and more reliably. Employees no longer need to manually search folders, emails, or PDFs. They can ask the knowledge base directly.
Different data formats can be included as well. Besides PDFs and Office documents, emails, attachments, images, scans, and screenshots can be processed. OCR and text recognition make content usable even when it previously existed only as an image or scan.
This reduces research effort, accelerates internal processes, and makes existing company knowledge more widely available.
The result is a central, secure, and AI-ready knowledge base. Companies keep control over their data, can manage access by role, and make existing knowledge efficiently usable.
This is not just a chatbot. It is a structured knowledge platform that understands documents, makes information findable, and supports employees in daily operations.
Company knowledge is visible only where it is needed. Roles, approved sources, and the operating model can be defined together.
AI-powered document analysis with a knowledge base makes existing company knowledge faster, safer, and easier to use. RAG, semantic search, OCR, vector databases, and role-based access create a platform that directly supports employees in daily work.
Instead of searching manually, teams can ask directly and receive answers based on their own documents. Scattered knowledge becomes a structured, secure, and intelligent knowledge base.