Knowledge Files show which sources are connected, which metadata was extracted, which permissions apply and which documents still need review. That keeps AI traceable before it answers.
Connect DMS, SharePoint, email, ERP and file shares
Check permissions and retention before indexing
Review queue for uncertain or blocked sources
Cited knowledge chat
A knowledge chat that backs every answer with a source
Staff ask in natural language — the AI answers only with evidence from approved documents, shows source and passage and routes sensitive cases for approval.
Answer with source, section and date
Permissions checked before retrieval
Review queue for uncertain answers
Deep Knowledge · Knowledge graph
Your documents become a connected knowledge graph
The AI extracts people, locations, contracts, deadlines and projects from your documents and links them into a searchable graph. Connections become visible across individual files — relationships that plain full-text search leaves hidden.
Entities, not text snippetsContracts, people, locations and projects are captured as connected nodes — not as loose search hits.
Places and relationships surfacedWhich person belongs to which project, which location to which contract — the graph makes it traceable.
Permissions stay intactEven in the graph, users only see nodes and edges they are entitled to — no detour around permissions.
Source layer
Documents, email and business systems provide only reviewed sources with metadata and revision.
Entities & edges
People, companies, projects, dates and contracts are connected with cited relationships.
Permission filter
Role permissions apply before search, graph view and AI answer.
Review & correction
Uncertain relationships enter a queue where specialists confirm or correct them.
Modules
Document & Knowledge AI for search, review and workflow automation
This example implementation combines RAG search, document classification, review rules and approval workflows in one controllable business application.
Cited knowledge search
Answers link back to the document, section and source passage; uncertain matches are clearly marked.
Document review
Contracts, proposals, policies or emails are checked against business rules and prepared as review proposals.
Workflow triage
Incoming documents are classified, prioritised and routed to the right role, team or existing ticket system.
Controlled operation
Hosting, model access, roles, logs and retention rules are planned around your privacy requirements.
Use cases
Document & Knowledge AI for concrete business functions
The solution is not meant as a generic chatbot. It is built around concrete document types, roles, approvals and follow-up actions — for legal, purchasing, support, finance, HR or project teams.
Legal and contract management
Check clauses, deadlines, liability points and deviations against internal rule libraries.
Purchasing and supplier files
Search proposals, price commitments, framework agreements and supplier communication with citations.
Support and inbox
Classify emails, detect urgency and route tickets to the right role with explanations.
Finance and invoice review
Match invoices, purchase orders and cost centers without pushing sensitive data into generic tools.
HR and policy knowledge
Make internal rules findable, but only for people with the right permissions.
Project and quality files
Connect project files, inspection reports, approvals and evidence across document boundaries.
AI asset · Document review
From files, contracts and policies to source-grounded AI review
The page does not rely on abstract screens only. This example shows how a business team works with real documents: sources are checked, passages are marked, risks move into a review queue and only approved results are passed to downstream systems.
Source
cited
Review
open
Rights
checked
Review flow · Contract, policy, inbox
Source
Analysis
Approval
Contract review with marked sourcesPrinted files, DMS sources and AI review become one traceable workflow.
Sources before answers
DMS · roles · index · audit
Knowledge Files control sourcesEvery source gets metadata, permissions, index status and human approval.
Rules, not guesswork
Clauses · deadlines · deviations
Contract review with rule libraryClauses, deadlines and deviations are prepared as suggestions, not decided automatically.
Triage with approval
Inbox · ticket · ERP · DMS
Inbox and system handoffClassification, priority and target system stay visible before a case moves on.
Implementation
From document collections to a production-ready AI workflow
01 · Scope
Map documents and risks
Which documents may be used, which sources are authoritative and which answers require explicit approval?
02 · Model
Build data model and roles
Document types, metadata, permissions, citations and audit events become the stable foundation.
03 · Validate
Measure answer quality
Test questions, edge cases and manual reviews show where the AI is reliable and where it must stop.
04 · Operate
Monitor and extend
Usage, errors, costs and new document types are monitored continuously after launch.
Custom build vs. generic AI tooling
Custom document & knowledge AI compared with Microsoft 365 Copilot and DMS search
Off-the-shelf products like Microsoft 365 Copilot or your existing DMS/SharePoint search make sense for small document sets, low risk and simple questions. Custom document & knowledge AI becomes valuable when permissions, citations, review rules, approvals and business systems have to work together exactly around your processes.
Custom-built by Bley IT
Microsoft 365 Copilot
DMS or SharePoint search
Source citations down to passage level
Role permissions per document type
Business review rules and approvals
Integration with tickets, CRM or ERP
Low up-front investment
EU-cloud or on-premise operation
Honest take: for simple Q&A over low-risk content, a standard tool is often faster. A custom build makes sense when wrong answers, wrong permissions or missing evidence create operational risk.
Features by area
Every feature, organised by area
The solution is organised into clear working areas. Open an area to see its workflows, the data it needs and the in-depth sub-pages in detail.
Frequent questions about custom document & knowledge AI
Can document & knowledge AI respect internal permissions?
Yes. Permissions are checked before retrieval, not only after an answer is generated. Users only see sources, documents and results they are allowed to access.
How do you prevent the AI from inventing answers?
Answers are tied to sources, uncertainty is surfaced and critical workflows require human approval. Test questions and failure types are reviewed before launch.
Does the system need to start with every document?
No. A clearly scoped starting set is better, such as contracts, policies or support emails. Additional document types can be added iteratively.
Which systems can be integrated?
Typical integrations include DMS, SharePoint, email, ticketing, CRM, ERP, file shares and business applications. The key question is where AI can measurably reduce work in an existing process.
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