Internal Knowledge Management System
Semantic search over the documents a company already has
- Type
- Semantic search API
- Users
- Internal teams + compliance officers
- Focus
- Retrieval + auditability
- Industry
- Enterprise / internal tooling
The challenge
Company knowledge ends up scattered across policy PDFs, HR handbooks and spreadsheets on a shared drive, where the only way to find anything is to already know the filename. Keyword search misses documents that phrase the same idea differently, and once staff do find an answer there's no record of who read which policy — the exact thing compliance audits ask for.
Our approach
Documents are chunked on upload, embedded with a sentence-transformer model and stored as vectors in PostgreSQL via pgvector, so a plain-language question matches on meaning. Every result carries its source filename back to the user for verification, and every view, search and upload writes to an audit log — treating traceability as part of the search flow rather than a bolt-on.
What it does
Semantic and hybrid search
Vector similarity over document chunks, combined with keyword matching, so natural-language questions return the right passage even when the wording differs.
Multi-format ingestion
PDF, Word, Excel and CSV files are parsed, chunked and embedded automatically on upload, with auto-suggested categories and tags.
Source-traceable results
Every result returns its original filename and file type, so staff can open the source document and verify an answer against the record.
Audit trail and access logs
Views, searches, uploads and deletions are logged with user, timestamp and IP address, queryable by administrators.
GDPR and retention tooling
Per-user data export and deletion endpoints, plus scheduled retention policies that archive or remove documents once they age past their category's limit.
Search analytics and knowledge gaps
Dashboards surface search trends, popular documents and zero-result queries — showing what people look for that the knowledgebase doesn't yet answer.
Conversational RAG
Optional retrieval-augmented chat sessions summarise and answer over the retrieved documents rather than returning a raw result list.
What it changes
- Documents found by meaning instead of exact filename or keyword
- Every document access logged and queryable for compliance review
- Missing content identified from real zero-result searches
- Stale documents archived automatically by policy instead of by hand
Built with
- Python
- FastAPI
- PostgreSQL
- pgvector
- SQLAlchemy
- Alembic
- sentence-transformers
- LangChain
- Docker
Want something like this built for you?
Tell us what you're running today and we'll scope it — starting with a fixed-price discovery.