Document Chat (RAG)
Search large document sets via chat instead of manually digging through contracts and reports.
Contracts, standards and reports are often hundreds of pages long and scattered across multiple archives. Retrieval-augmented generation retrieves the relevant passages and formulates precise answers with references to the original location, keeping every statement verifiable.
- Much faster contract and report research.
- Fewer errors from overlooked clauses.
- Traceable source references per answer.
Time spent on document research
Time to value
Retrieval accuracy in testing
More searchable documents
Calculated from average research time per case, multiplied by case volume in the department.
- 01
Document audit
Capture relevant document types and archive systems.
- 02
Indexing
Break DMS content into searchable knowledge chunks.
- 03
Retrieval tuning
Optimize search quality using evaluation sets of real questions.
- 04
Guardrails & approvals
Secure access rights per document class.
- 05
Rollout
Deployment in the department with a feedback loop.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
DMS
Contracts, quotes and internal reports.
Standards & guidelines
Technical standards and regulatory requirements.
Contract metadata
Terms, parties and deadlines for context.
SharePoint archives
Project documents and minutes.
Scans/OCR archives
Digitized legacy archives as an additional source.
Roles & permissions concept
Controls which document classes each role may view.
Legal department
Faster contract review with evidence.
Procurement
Quick access to previous terms.
Quality management
Direct access to relevant standard sections.
IT security
Controlled access by document class.
Much faster contract and report research.
Fewer errors from overlooked clauses.
Traceable source references per answer.
Access to knowledge across department boundaries.
Scales with growing document volumes.
With a solid data foundation this use case gets faster, cheaper and far more stable.
Well-tagged DMS structures speed up indexing.
A central permissions model prevents unintended access.
Existing OCR pipelines supply additional sources.
An EU-hosted vector database ensures compliance.
Internal enterprise GPT
Uses the same indexing and guardrail infrastructure.
Quote & tender assistant
Previous quotes can be retrieved via chat.
Knowledge management & onboarding
Document knowledge becomes accessible to new employees.
Internal Enterprise GPT
A company-owned chat assistant that reliably answers questions on processes, policies and expertise.
Service & Repair Assistant
Technicians get precise repair guidance on-site, drawn from manuals and ticket history.
Quote & Tender Assistant
Draft quotes and tender documents faster, backed by past deals and price lists.