Service
RAG & Knowledge Systems
Ask questions of your own documents and get answers that show their sources.
The problem
Knowledge sits in PDFs, wikis, tickets and drives that nobody can search well. Generic chatbots make things up. A retrieval (RAG) system answers only from your content and shows where each answer came from.
What you get
- 01Ingestion of your sources, kept in sync as they change.
- 02Search tuned to your content: chunking, hybrid search, reranking.
- 03Answers that cite the passages they used.
- 04Access rules, so people only see answers from documents they're allowed to read.
- 05An accuracy report on a set of real questions from your team.
Typical projects
Internal knowledge assistant
For policies, manuals or past projects.
Customer-facing help
Answers from your documentation.
Specialist document analysis
Regulatory, financial or technical documents.
How it's built
- Python
- FastAPI
- Vector databases (pgvector, Qdrant)
- Embedding and reranking models
- LLM APIs
FAQ
Is my data used to train models?
No. API providers are configured not to train on your data, and self-hosted models are an option.
How do I know answers are right?
Every answer shows its sources, and accuracy is measured against your own questions.
How does a project start?
With a free call about the problem. Then you get a written plan: deliverables, timeline, and a fixed price or a clear estimate.
Will I see progress before the end?
Yes. Working software every week on a preview link, not a big reveal at the end.
What do I get at handover?
The code, tests, documentation and a walkthrough. You own all of it.
Other services
Have a problem AI might solve?
Describe it in a few lines. You'll get an honest answer, including when AI isn't the right tool.