Scattered company knowledge
Teams spend time searching documents, systems and shared folders for answers.
Enterprise Search · RAG · Citations · Access Control
Give teams AI answers grounded in approved company knowledge.
ENIGMA builds Knowledge AI and RAG systems that retrieve approved company information, respect permissions, cite sources and support workflows across support, sales, operations, HR, finance and management.
Knowledge AI & RAG by ENIGMA
How ENIGMA structures enterprise knowledge systems
Source layer: approved documents, policies, SOPs, websites, databases, CRM, ERP, tickets and internal APIs.
Retrieval layer: chunking, metadata, embeddings, keyword search, hybrid retrieval, ranking and reranking.
Permission layer: user, role, department, customer, project, location and document-level access rules.
Answer layer: grounded responses, citations, source snippets, confidence states and “not enough information” behavior.
Governance layer: source authority, versioning, re-indexing, evaluation, audit logs, monitoring and feedback loops.
Why Knowledge AI Matters
Most companies have knowledge. Few can retrieve it reliably when work is happening.
Policies live in folders, customer context sits in CRM, pricing rules sit in spreadsheets and operational know-how often stays with senior team members. RAG creates a governed way to retrieve and use that knowledge.
Unsupported AI answers
Generic model responses are risky when business decisions depend on current facts.
Permission risk
Not every user should retrieve every policy, customer record or finance document.
Outdated knowledge
Old files can produce wrong answers unless ownership, versions and refresh rules are explicit.
Repeated support questions
Teams repeatedly answer the same operational and product questions from approved material.
No audit trail
Leaders need to know which sources were retrieved and what evidence supported an answer.
RAG Operating Model
From question to evidence-backed answer.
A strong RAG system does more than vector search. It identifies the user, filters sources, retrieves evidence, builds grounded context and preserves source visibility.
Question
Capture user intent, task and business context.
Identity
Resolve role, department and permission scope.
Retrieve
Search approved sources with semantic and exact-match signals.
Rank evidence
Choose relevant, current and permission-allowed context.
Ground answer
Generate from retrieved evidence with citations and constraints.
Log & improve
Capture feedback, failures, source gaps and evaluation signals.
Capability Modules
What a Knowledge AI and RAG system can include.
The architecture is shaped around knowledge sources, access rules, answer-quality requirements and the workflow where responses will be used.
Knowledge Sources
Connect the knowledge your teams already depend on.
Map documents, records, databases and APIs into a governed knowledge architecture with explicit source ownership.
- PDFs, policies, SOPs and manuals
- CRM, ERP, tickets, product and inventory data
- Approved websites, catalogues and shared knowledge
- Source authority and freshness rules
Retrieval Engine
Retrieve the right context before the AI answers.
The retrieval layer determines which information the model can see using semantic, keyword, metadata and permission-aware signals.
- Chunking and indexing strategy
- Embeddings and vector search
- Hybrid search and metadata filters
- Ranking, reranking and retrieval evaluation
Access Control
Make knowledge useful without making it uncontrolled.
Enterprise RAG needs security boundaries so retrieval follows the same access logic as the business.
- Role and department-level access
- Customer, project and location filters
- Restricted HR or finance knowledge
- Audit logs for questions, sources and answers
Citations & Evidence
Let users see why the AI answered that way.
Users can be shown source references, snippets, dates and confidence states so answers remain auditable.
- Document and section references
- Source snippets and evidence context
- “Not enough information” behavior
- Feedback capture for answer improvement
Workflow Actions
Move from answer to guided business action.
Knowledge AI can support support agents, sales teams, field teams and managers with contextual next steps while keeping human approval where needed.
- Support assistant with ticket context
- Sales assistant with product and policy knowledge
- SOP assistant for field and service teams
- Management assistant for reports and exceptions
Monitoring & Evaluation
Measure answer quality before and after rollout.
Production Knowledge AI needs test questions, expected answer behavior, source checks, usage analytics and improvement loops.
- Golden-question evaluation
- Retrieval and source relevance review
- Usage, failure and escalation analytics
- Re-indexing and knowledge refresh cycles
RAG Architecture
The complete architecture behind trusted answers.
RAG is not only vector search. It combines source governance, indexing, retrieval, permission checks, prompt control, citations, evaluation and user experience.
Illustrative RAG Workflow
QUESTION
ACCESS
EVIDENCE
ANSWER
Question to permission-aware evidence and cited response.
User asks an operational question
The system captures the question, user identity and business context.
Permission filter is applied
Only documents and records allowed for the user are eligible for retrieval.
Relevant sources are ranked
The retrieval layer selects current, high-relevance context.
Response includes citations
The model answers from retrieved evidence and preserves source references.
This is an illustrative enterprise RAG pattern, not a fabricated client result.
What ENIGMA Delivers
A governed knowledge system—not a document-upload demo.
The engagement can include source mapping, retrieval architecture, permissions, answer experience, citations, evaluation and rollout controls.
Knowledge Audit
Source inventory, trusted owners, content gaps, use cases and access requirements.
Retrieval Design
Chunking, indexing, metadata, search, ranking, reranking and answer behavior.
Access & Citation Model
Role controls, source authority, citations, audit logs and update policies.
Evaluation & Rollout
Test questions, analytics, feedback workflows, deployment and handover.
Engineering Process
How ENIGMA builds Knowledge AI and RAG systems.
Use-case Discovery
Define who asks, what they ask and where answers affect work.
Source Mapping
Identify documents, systems, owners, permissions and source authority.
Retrieval Build
Create indexing, embeddings, search, filters and ranking.
Interface Design
Build chat, portal, dashboard or embedded-assistant experiences.
Evaluation
Test retrieval quality, answer accuracy, access and citations.
Operate & Improve
Monitor usage, refresh knowledge and improve retrieval over time.
Frequently Asked Questions
Common questions about Knowledge AI and RAG.
Knowledge AI uses company documents, databases and approved knowledge sources to answer questions and support workflows. RAG, or retrieval augmented generation, retrieves relevant source content before generating an answer so responses can be grounded in company knowledge.
A normal chatbot may answer from general model memory or scripted responses. A Knowledge AI and RAG system retrieves approved company information, respects access rules, cites sources and can connect answers to business workflows.
Yes. ENIGMA can design RAG systems that show citations, document names, section references, source confidence and the context used to generate an answer.
Yes. Access can be controlled by role, department, customer, location, project or document category so users only retrieve knowledge they are allowed to see.
Knowledge AI systems can connect documents, PDFs, policies, SOPs, product catalogues, CRM records, ERP data, tickets, websites, databases, shared drives and internal APIs depending on the approved architecture.
Not always. Many RAG systems use retrieval, embeddings, indexing and prompts with existing models. Fine-tuning or private models are considered only when the use case, privacy requirements or performance needs justify them.
Knowledge freshness is handled through source ownership, document versioning, scheduled re-indexing, expiry rules, approval workflows and clear source authority rules.
Typical deliverables include knowledge audit, source mapping, access design, retrieval architecture, indexing pipeline, answer interface, citation behavior, evaluation tests, monitoring and deployment support.
Knowledge Architecture Discovery
Need company knowledge to become searchable, cited and permission-aware?
We can map your sources, owners, access rules, freshness requirements, answer-quality goals and integration points before defining the right RAG architecture.