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.
Retrieval-augmented generation combines retrieval from a knowledge source with generated responses grounded in the retrieved material.
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.
Evaluate retrieval relevance, access permissions, source freshness, citations, and answers when evidence is missing.
Retrieval quality and answer quality need separate tests.
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.
What clients say about us
Trusted for responsiveness, delivery quality, and ownership.
Feedback from clients who have worked with Etelligens across application development, web platforms, branding, and complex software delivery.
01
Praised the team’s responsiveness, willingness to go beyond the agreed scope, and the quality of the completed application.
Joshua HarrisEtelligens client
02
Highlighted the quality of the website, strong troubleshooting, fast understanding of requirements, and a positive overall delivery experience.
Dean EdelsonEtelligens client
03
Commended the booking-application team for identifying overlooked issues, exceeding expectations, and delivering a polished finished product.
Dr. Matthew MaggioEtelligens client
04
Said the team captured the brand’s identity effectively, communicated promptly across Western time zones, and earned continued work on product and service branding.
Joel LogicEtelligens client
05
Described the team as highly capable and accessible, crediting them with rescuing a difficult software project and consistently going the extra mile to deliver on time.
SargeEtelligens client
06
Highlighted faster-than-expected delivery, close adherence to requirements, and strong communication throughout the web-development project.
Christopher SandsEtelligens client
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Turn enterprise knowledge into a grounded AI experience users can verify.
Talk to our AI team ↗Frequently asked questions
RAG Development Services: questions before you start
Practical answers on scope, delivery choices, and acceptance.
Retrieval-augmented generation combines retrieval from a knowledge source with generated responses grounded in the retrieved material. The agreed scope can include AI delivery scope, data readiness, evaluation, and rollout controls.
Evaluate retrieval relevance, access permissions, source freshness, citations, and answers when evidence is missing. Bring the current workflow, important constraints, and the decision or user outcome you need to improve.
Retrieval quality and answer quality need separate tests. Ask the delivery team to explain the alternatives, exclusions, and evidence that would change its recommendation.
Scope, integration dependencies, data readiness, access approvals, and acceptance requirements determine the estimate. For this work, plan explicitly for AI delivery scope, data readiness, evaluation, and rollout controls. Request milestones and assumptions rather than an unsupported fixed-price promise.
Agree acceptance evidence before implementation. Evaluate retrieval relevance, access permissions, source freshness, citations, and answers when evidence is missing. Record known limitations, unresolved risks, ownership after handoff, and the next review point.
Delivery evidence
Connect the scope to real work.
Retrieval quality and answer quality need separate tests. Related capabilities include AI Governance Consulting.