Starting point
Procedures, decisions and operational knowledge were spread across files and people's memory. Traditional search required knowing the document name and the right keywords, while AI answers without sources were difficult to trust.
All case studies
RAG platform · Company knowledge
DocBase
A system that turns scattered documents into searchable company knowledge. Employees ask questions in natural language and receive an answer with the exact file, page and quote needed to verify it.
How the solution works
The result is a multi-tenant RAG platform. Documents are split into chunks and indexed semantically in PostgreSQL with pgvector. A question searches only the information available to the user's company and access level, then the model builds its answer from the retrieved passages.
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What was built
Answers that can be verified
Every answer includes its sources, document name and page number. The user can open the material on which the answer is based.
Complete document pipeline
PDF and DOCX files move through storage, processing, text extraction, chunking and vector indexing without manual tagging.
Access reflecting the company
Row Level Security isolates company data, while access levels restrict documents and functions to the appropriate user groups.
History and usage control
The system stores conversations, messages and activity logs. The organisation panel shows users, documents and core usage statistics.
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What the system changes
- One place to ask questions about company documents.
- A source and quote with every answer instead of blind trust in the model.
- Knowledge available without depending on one specific employee.
- An architecture ready for more companies, roles and data sources.
Facing a similar process?
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During a free call, we will walk through the process, data and current tools. You will receive a recommendation for the first sensible scope.