Knowledge discovery
Inventory sources, owners, formats, permissions, freshness, metadata, and authoritative content.
etelligensAi · Retrieval-augmented generation
Build RAG systems that answer from trusted enterprise knowledge with traceability and access control.
Etelligens designs retrieval-augmented generation solutions that connect language models to governed enterprise content using ingestion, metadata, search, reranking, permissions, citations, and continuous evaluation.
Business-value firstSecurity & governance by designProduction engineeringMeasured adoption
Why it matters
RAG quality depends as much on content engineering and retrieval as it does on the language model.
We start with the questions users need answered, the authoritative sources, permission boundaries, freshness requirements, and evidence needed to trust a response.
Pipelines are designed for parsing, chunking, enrichment, metadata, indexing, hybrid retrieval, reranking, context assembly, citations, and content updates.
Evaluation measures retrieval quality and answer quality separately so teams can diagnose whether a problem comes from source content, search, context construction, or generation.
Capabilities
What Etelligens delivers.
RAG can support internal knowledge, customer support, regulated documentation, technical content, policy search, and AI products.
Ingestion & enrichment
Parse, clean, chunk, classify, tag, extract structure, and preserve provenance across content types.
Retrieval architecture
Implement vector, keyword, hybrid search, metadata filters, reranking, query rewriting, and source selection.
Permission-aware context
Enforce identity, role, tenant, document, and field-level access in retrieval and response construction.
Citations & grounded UX
Surface evidence, links, confidence cues, and source context so users can verify important answers.
RAG evaluation & monitoring
Measure retrieval recall, relevance, groundedness, answer quality, latency, cost, and content freshness.
Enterprise use cases
Where this capability creates value.
Reliable enterprise RAG is a knowledge platform problem, not simply a vector database configuration task.
Enterprise knowledge assistant
Help employees search policies, procedures, product information, research, and internal documentation.
Customer support knowledge
Ground self-service and agent assist in approved service content with citations and version awareness.
Technical documentation
Search and synthesize engineering documentation, manuals, runbooks, specifications, and architecture knowledge.
Regulated information access
Support traceable answers over controlled content where source provenance and permissions are essential.
Delivery model
From opportunity to reliable production.
Our multidisciplinary team connects product strategy, data, AI engineering, application integration, security, quality engineering, and change management.
Inventory
Map source systems, content quality, permissions, user questions, update cadence, and evidence requirements.
Prototype
Compare chunking, embeddings, search, reranking, prompts, and models against representative questions.
Engineer
Build ingestion, retrieval, permissions, evaluation, citations, observability, and application integration.
Improve
Use failed queries and relevance signals to improve content, metadata, retrieval, prompts, and source coverage.