Problem → Knowledge AI response
Scattered Company Documents
Teams waste time searching policies, SOPs, product files and operational notes. RAG systems retrieve the most relevant approved source and expose it inside a controlled answer experience.
Knowledge AI & RAG
Give teams AI answers grounded in company knowledge.
ENIGMA builds Knowledge AI and RAG systems that retrieve approved company information, respect permissions, cite sources and help teams act faster across support, sales, operations, HR, finance and management workflows.
library_booksSource-grounded Answers
policyRole-based Knowledge
fact_checkCitations & Audit
Knowledge Layer
Documents, policies, SOPs, product data, CRM and ERP knowledge organized for retrieval.
Retrieval Layer
Search, embeddings, filters and context ranking before answer generation.
Governance Layer
Permissions, source authority, version control, logs and evaluation workflows.
neurology
RAG Engine
Retrieve → Ground → Answer
Sources
Company Knowledge
Policies, SOPs, PDFs, tickets, products, CRM and ERP records.
Indexing
Search + Vectors
Chunking, embeddings, metadata and permission-aware retrieval.
Governance
Access Control
Role, department, customer and document-level visibility rules.
Answer
Cited Response
Grounded answer with source references and audit history.
Why Knowledge AI matters
Most companies have knowledge. Few can retrieve it reliably when work is happening.
Policies sit in folders, customer context lives in CRM, pricing rules sit in spreadsheets and operational answers are trapped with senior team members. Knowledge AI creates a governed way to find and use that knowledge.
Problem → Knowledge AI response
Unsupported AI Answers
Generic answers are risky when business decisions depend on facts. Knowledge AI grounds responses in selected sources and can show document references behind the answer.
Problem → Knowledge AI response
Permission Risk
Not every employee should access every answer. Retrieval can be restricted by role, department, location, customer, document category or business unit.
Problem → Knowledge AI response
Outdated Knowledge
Old files often create wrong answers. Source authority, version control, re-indexing and approval workflows help keep knowledge current and traceable.
Problem → Knowledge AI response
Repeated Support Questions
Support, sales and internal teams repeat the same explanations. Knowledge AI can answer common questions from approved material and escalate when source confidence is low.
Problem → Knowledge AI response
No Knowledge Audit Trail
Leaders need to know what source produced which answer. The system can retain prompts, retrieved sources, answer versions and feedback signals for review.
Capability modules
What a Knowledge AI and RAG system can include.
The system is designed around your knowledge sources, access rules, answer quality needs and the workflow where answers will be used.
Knowledge Sources
Connect the knowledge your teams already depend on.
ENIGMA maps documents, records, databases and APIs into a usable knowledge architecture. The goal is not to upload everything blindly, but to identify which source is trusted for each business question.
- • PDFs, policies, SOPs, manuals and internal documents
- • CRM, ERP, support-ticket, product and inventory knowledge
- • Website pages, catalogues, pricing sheets and onboarding material
- • Source authority rules for conflicting or outdated information
Document libraryIndexed
CRM / ERP recordsConnected
Approved websitesSynced
Source authorityGoverned
Retrieval Engine
Retrieve the right context before the AI answers.
The retrieval layer decides what information the model should see. This can include semantic search, keyword search, metadata filters, ranking, reranking and permission checks.
- • Chunking and indexing strategy for different document types
- • Embeddings, vector search and metadata-aware filtering
- • Hybrid search for exact business terms and semantic meaning
- • Retrieval evaluation to reduce weak or irrelevant context
User questionIntent parsed
Metadata filterRole + Source
Context rankingMost relevant
Answer generationGrounded
Access Control
Make knowledge helpful without making it uncontrolled.
Enterprise RAG systems need security boundaries. ENIGMA designs role, department, customer, project and document-level controls so retrieval follows the same access logic as your business.
- • Role-based and department-based document access
- • Customer, project, location or branch-specific knowledge filters
- • Restricted answers for confidential finance or HR content
- • Audit logs for user questions, sources and responses
Sales teamSales docs only
Support teamTicket + SOP scope
Finance teamRestricted access
ManagementCross-system view
Citations & Evidence
Let users see why the AI answered that way.
For business use, the answer is not enough. Users need confidence, source references, document context, update date and sometimes a low-confidence warning when the answer should not be trusted.
- • Document references and source snippets
- • Confidence states and “not enough information” behavior
- • Source authority when multiple documents conflict
- • Feedback capture for continuous answer improvement
AnswerGenerated
Source 01Policy PDF
Source 02CRM record
User feedbackLogged
Workflow Actions
Move from “answer” to guided business action.
Knowledge AI can support real workflows: help support agents resolve tickets, help sales teams answer product questions, guide field teams through SOPs or help managers understand operational exceptions.
- • Support-agent assistant with ticket context
- • Sales-product assistant with pricing and policy knowledge
- • SOP assistant for field, installation or service teams
- • Management knowledge assistant for reports and summaries
Question answeredWith sources
Next actionSuggested
Human approvalOptional gate
System updateCRM / ERP / Ticket
Monitoring & Evaluation
Measure answer quality before and after rollout.
Production Knowledge AI needs evaluation. ENIGMA can define test questions, expected answer patterns, source checks, usage analytics and feedback workflows so the system improves over time.
- • Golden-question testing and answer evaluation
- • Source relevance and retrieval-quality review
- • Usage, failure, escalation and feedback analytics
- • Re-indexing, source updates and improvement cycles
Answer qualityEvaluated
Retrieval qualityMeasured
Feedback loopCaptured
Knowledge refreshScheduled
RAG architecture
The complete architecture behind trusted answers.
RAG is not only vector search. It is source governance, indexing, retrieval, prompt control, answer evaluation and user experience built around business risk.
1. Sources
Documents, records, websites, databases and APIs
2. Index
Chunking, metadata, embeddings and search preparation
3. Retrieve
Permission-aware ranking and source selection
4. Answer
Grounded response, citations, logs and feedback
What ENIGMA delivers
A governed knowledge system, not a document-upload demo.
The deliverable is the knowledge architecture, retrieval pipeline, permission model, user experience and operating process needed for reliable enterprise use.
Discovery
Knowledge Audit
Source inventory, trusted owners, content gaps, use cases and access requirements.
Architecture
Retrieval Design
Chunking, indexing, search approach, metadata rules, reranking and answer behavior.
Governance
Access & Citation Model
Role controls, source authority, citation patterns, audit logs and update policies.
Operations
Evaluation & Rollout
Testing questions, usage analytics, feedback workflows, deployment and handover.
Engineering process
How ENIGMA builds Knowledge AI and RAG.
01
Use-case Discovery
Define who asks, what they ask and where answers affect work.
02
Source Mapping
Identify documents, systems, owners, permissions and source authority.
03
Retrieval Build
Create indexing, embedding, search, filters and ranking pipeline.
04
Interface Design
Build chat, portal, dashboard or embedded assistant experience.
05
Evaluation
Test retrieval quality, answer accuracy, access rules and citation behavior.
06
Operate & Improve
Monitor usage, update 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.
Ready to organize company knowledge?
Let’s turn scattered documents and systems into trusted AI answers.
We start with knowledge sources, access rules and use cases before building the retrieval pipeline. The goal is controlled business knowledge, not a generic document chatbot.