# Knowledge and Data Systems

## Who this is for
- Organisations where institutional knowledge lives in folders nobody can
  find things in.
- Teams spending time answering the same internal questions repeatedly.
- NGOs and professional-services firms with case files, reports, or
  research that ought to be searchable but isn't.

## Problem
- Knowledge-management SaaS is expensive and creates new silos.
- Generic AI chat on top of a messy folder tree produces confident wrong
  answers.
- Without structure, a RAG system amplifies whatever existing disorder
  was there.

## What we do
- Audit existing document and folder state — identify what should be
  indexed, what should be archived, what should be rewritten.
- Build RAG systems with sources cited — every answer points back to the
  document it came from.
- Integrate internal search into the tools staff already use.
- Design folder and metadata conventions that scale.

## Outcomes
- Staff find answers in seconds rather than minutes.
- AI-generated summaries of internal documents, with citations.
- A knowledge base that stays organised as it grows.
- Clear separation between public, internal, and restricted information.

## How we work
- Start with an audit (fixed price), then scope the build from audit
  findings.
- Sovereignty-aware — sensitive documents stay on EU infrastructure, or
  locally hosted, depending on risk posture.
- See also: [AI Orchestration](/services/ai-orchestration/index.md),
  [Strategy and Advisory](/services/strategy-advisory/index.md).

## Next step
Book a discovery call to scope an audit.

Canonical page: https://crystallized.lu/services/knowledge-data-systems