Scattered documents
Policies, SOPs and reports live across multiple repositories.
Private RAG · Enterprise Search · Knowledge Governance
Turn scattered company knowledge into one secure, source-backed AI system.
ENIGMA structures private knowledge systems around approved documents, permission-aware retrieval, RAG, source citations, governance and internal AI workflows—without unsupported answers or fabricated productivity claims.
Private knowledge systems by ENIGMA
How ENIGMA structures enterprise knowledge AI
Source layer: PDFs, SOPs, policies, manuals, CRM, ERP, databases, websites and approved repositories.
Retrieval layer: chunking, metadata, embeddings, keyword + semantic search, filters, ranking and reranking.
Permission layer: role, department, customer, project, location and document-level access.
Answer layer: grounded responses, citations, evidence snippets and insufficient-evidence handling.
Governance layer: source authority, versions, freshness, feedback, monitoring, audit and re-indexing.
Case Context
The knowledge-access problem this case study solves.
Knowledge often exists, but it is scattered across folders, systems and people. The challenge is trusted retrieval under the right access rules.
Repeated questions
Employees ask the same operational questions again and again.
Outdated knowledge
Old copies circulate without clear source authority.
No source trust
Users cannot verify the evidence behind generic answers.
Permission risk
Sensitive HR, finance and customer knowledge needs strict access.
Slow onboarding
New team members depend too heavily on senior staff.
Case Study Breakdown
Problem → Architecture → Implementation → Outcome.
The case study focuses on source governance, RAG retrieval, access control and operational trust—not invented client metrics.
Knowledge Access Problem
Teams lose time when company knowledge is scattered and hard to verify.
Employees search old folders, ask seniors and compare versions because there is no governed retrieval layer.
- Scattered files and duplicated knowledge
- Weak search across internal sources
- Unclear source authority and freshness
- Permission risk for sensitive content
Knowledge AI Architecture
A private knowledge layer connects sources, permissions and grounded answers.
Approved sources are indexed, filtered by permission and retrieved before an answer is generated.
- Document ingestion and metadata mapping
- Hybrid search and ranking
- Permission-aware retrieval
- Cited responses and evaluation
Implementation Approach
The build focuses on trusted answers, not just a chatbot interface.
Implementation includes connectors, indexing, retrieval logic, citations, admin controls, feedback and monitoring.
- Source connectors and ingestion workflows
- Chunking, embeddings and reranking
- Role/document permission controls
- Evaluation and feedback monitoring
Operational Outcome
Internal knowledge becomes easier to find, verify and govern.
The value is trusted access and a reusable foundation for internal assistants and enterprise automation.
- Faster access to approved knowledge
- Reduced dependency on individual memory
- Better source verification
- Reusable foundation for AI agents
Knowledge Architecture
A practical architecture for private enterprise knowledge systems.
Source authority, retrieval quality, access control and grounded answers are designed as one operating model.
Implementation Modules
What the private knowledge system can include.
Document Ingestion
PDFs, SOPs, policies, manuals, reports, CRM notes and approved repositories.
RAG & Search Layer
Chunking, embeddings, hybrid search, ranking and citations.
Permissions & Governance
Role access, department access, audit controls and sensitive-data boundaries.
Usage & Feedback
Unanswered questions, freshness, feedback and admin reporting.
Frequently Asked Questions
Common questions about private knowledge systems.
It explains how scattered company documents, policies, SOPs, CRM notes and internal data can be converted into a private knowledge system with search, RAG, source-grounded answers and role-based access.
No. The page avoids fake client names, productivity percentages, adoption numbers and ROI claims. It presents a credible implementation pattern.
It can connect with PDFs, SOPs, manuals, CRM, ERP, HRMS, databases, ticket systems, websites, portals and approved internal repositories.
RAG retrieves relevant approved sources before generating an answer, so responses are grounded in company knowledge.
Yes. Access can be controlled by department, role, team, location, customer, project or document-level permissions.
Yes. Answers can include document names, sections, snippets or source citations so users can verify the evidence.
Discovery should map source systems, permissions, source authority, freshness, sensitive data, search needs, integrations, governance and review workflows.
Need a Private Knowledge System?
Turn company knowledge into a secure, source-grounded AI layer.
We start with your documents, permissions, source authority, search needs and governance rules before defining the retrieval architecture.