DigiEdze AI · Enterprise RAG & Search
Turn Enterprise Knowledge into Intelligence
Ground AI in your documents, databases, policies, contracts, runbooks and institutional knowledge with retrieval that respects the permissions of every source system it reads.
- vector search
- hybrid search
- GraphRAG
- multimodal RAG
- re-ranking
- citation
- access-aware retrieval
Reference architecture
- 07Query understanding
- 06Access-control filter
- 05Hybrid retrieval — vector + keyword + graph
- 04Re-ranking
- 03Context assembly with citations
- 02Model reasoning
- 01Groundedness evaluation
What Changes When This Goes Wrong?
The answer exists somewhere in the estate, and nobody can find it in time.
Naive retrieval returns plausible passages from documents the user was never entitled to see.
Without citations, no regulated function can act on what the system says.
Enterprise RAG and Search Capability Grid
Hybrid retrieval
Dense vector search combined with keyword and metadata filtering, because enterprise queries contain identifiers that embeddings handle poorly.
Access-aware retrieval
Permissions enforced at query time against source-system entitlements — retrieval never becomes a data-exfiltration path.
GraphRAG
Knowledge-graph traversal for questions that require relationships across entities rather than a single passage.
Multimodal RAG
Retrieval over tables, diagrams, scans and images alongside text.
Re-ranking and citation
Cross-encoder re-ranking for precision, with every claim traceable to a source and a location within it.
Freshness and lifecycle
Incremental indexing, deletion propagation and staleness monitoring, so the index reflects reality.
How it works
This is the retrieval stack that powers accurate, grounded AI responses over enterprise content for structured relationships, and re-rankers for precision.
Technology
- Qdrant and comparable vector stores
- Hybrid search engines
- Knowledge graphs
- Cross-encoder re-rankers
- Document parsing pipelines
- Embedding models
Use Cases
Where This Lands in Practice?
Insurance
Underwriters retrieve the governing clause and the precedent decisions together, with both cited.
Healthcare
Clinical and operational staff query protocols and guidance under strict access segregation.
Energy & Utilities
Field engineers search asset histories, standards and incident reports from one interface.
Outcomes
What We Measure
Retrieval & Grounding Outcomes tracks whether the system reliably surfaces the right information and whether its answers are actually backed by that information, rather than fabricated.
- Retrieval precision and recall
- Groundedness score
- Citation coverage
- Time to find a governing answer
Questions
Enterprise RAG & Search, Answered Directly
Does RAG remove the need for fine-tuning?
They solve different problems. Retrieval supplies facts that change; fine-tuning teaches format, terminology and task behaviour that stays stable. Most production systems use retrieval first, and fine-tune only once retrieval quality has plateaued.
How do you enforce permissions in retrieval?
Documents are indexed with their entitlement metadata, and every query is filtered against the caller's live entitlements before ranking. Post-filtering after retrieval is not sufficient — it leaks existence and content through ranking behaviour.
What breaks enterprise RAG most often?
Document preparation. Tables flattened into prose, headers lost, revisions not deduplicated and permissions not captured at ingest cause more failures than model choice ever does.
Related services
Data & Knowledge
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Ontologies, graphs and semantic layers that become AI memory.
ExploreData & Knowledge
AI Data Engineering
Pipelines, lakehouse and unstructured data foundations for AI.
ExploreIntelligence
Generative AI
Grounded, secure GenAI applications wired into enterprise data.
ExploreChoose your starting point
Where Are You Today?
Enterprise AI conversations stall when the entry point is wrong. Pick the sentence that matches your situation.
We have an idea.
→ AI STRATEGY
We have a POC.
→ AI PRODUCTIONIZATION
We have an AI application.
→ LLMOPS / AGENTOPS
We need autonomous workflows.
→ AGENTIC AI
Our AI is too expensive.
→ AI FINOPS
Our AI can't reach enterprise knowledge.
→ RAG & KNOWLEDGE
We can't send data to public AI.
→ SOVEREIGN AI
We need to run AI securely.
→ AI SECURITY
Design Your Enterprise RAG
Implement RAG by talking with our experts, who can engineer the right-fit intelligence for your enterprise.
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