How is RAG accuracy measured?

Retrieval recall and precision on a labelled question set, plus groundedness and citation-correctness scoring on the generated answers.

Does RAG remove the need for fine-tuning?

For knowledge freshness and citation, yes in most cases. Fine-tuning still helps with format, tone and narrow classification tasks.

How is sensitive content kept out of answers?

Source-level access control carried into the index, entitlement filtering at query time, redaction in the ingest pipeline, and audit logs of every retrieved chunk.

What is knowledge AI consulting?

Designing the retrieval, permission and evaluation layer that lets AI answer questions from your own content accurately, with citations and access controls intact.

What do RAG implementation services include?

Source connectors and ingestion, chunking strategy, embedding and index design, hybrid retrieval and re-ranking, entitlement enforcement, citation rendering and evaluation.

When is graph RAG implementation worth it?

When questions require multi-hop reasoning across related entities — policies to products, providers to contracts — and vector similarity alone returns plausible but incomplete context.

How do you stop RAG answers from hallucinating?

Grounded prompting with mandatory citations, retrieval confidence thresholds, refusal behaviour when evidence is weak, and groundedness scoring in the evaluation harness.

How do you enforce permissions in enterprise RAG?

Entitlements travel with the document through ingestion and are enforced at query time, so users only retrieve passages they are already authorized to read.

Enterprise RAG

Knowledge AI consulting and enterprise RAG implementation.

Pronix delivers knowledge AI consulting and enterprise RAG consulting for organizations whose answers live across SharePoint, Confluence, ServiceNow, EHRs, policy libraries and product documentation. Our RAG implementation services cover ingestion, chunking, hybrid and graph RAG retrieval, entitlement-aware access, citations and evaluation — so enterprise RAG implementation produces answers people and auditors can trust.

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Enterprise AI stackreference architecture

Answer & Citation Layer

GroundingCitesRefusal

Retrieval

HybridRerankFilters

Index

VectorKeywordGraph

Ingestion

ParseChunkEnrich

Entitlements

ACLsPIIAudit

How we deliver

Assess → Design → Pilot → Implement → Scale → Operate.

One delivery model that moves from strategy to production without handoffs — with managed operations built in.

See how we deliver

Implementation · Enterprise RAGphased delivery

Assess

Corpora, entitlements, question inventory, success criteria.

wk 1

Design

Index topology, chunking strategy, retrieval and eval plan.

wk 3

Build

Ingestion pipelines, hybrid retrieval, reranking, citations.

wk 5

Evaluate

Golden set scoring, red-teaming, refusal and safety tests.

wk 7

Launch

Canary rollout, monitoring, feedback capture.

wk 9

Operate

Refresh, tuning, cost and latency optimisation.

wk 11

8–12 wks

To first production RAG domain

90%+

Answer faithfulness on the golden set

100%

Queries entitlement-filtered at runtime

Retrieval layer reused across use cases

The enterprise challenge

A demo retrieves. Production has to be right, permissioned and provable.

Vector search is the easy 20%. The remaining 80% is content quality, entitlement enforcement, freshness and evaluation — the work that decides whether legal, risk and the business will actually let the system go live.

Ungoverned content
Duplicated, stale and conflicting documents produce confident wrong answers no prompt tweak can fix.
Permissions ignored
Pilots index everything for everyone — an access-control incident waiting to be discovered at review.
No quality baseline
Without a golden set and faithfulness scoring, 'it seems better' is the only available evidence.

Capabilities

The full retrieval stack, built once and reused.

Content & entitlement discovery

Source inventory, ownership, freshness SLAs and the access model each corpus must honour.

Ingestion & chunking

Layout-aware parsing for PDFs, tables and diagrams, semantic chunking, metadata enrichment and incremental sync.

Hybrid retrieval & reranking

Vector plus keyword retrieval, metadata filtering and cross-encoder reranking tuned per domain.

Grounding & citations

Citation-required answering, answerability checks and explicit refusal when evidence is insufficient.

Evaluation engineering

Golden sets, faithfulness, context precision and answer relevance — run in CI and monitored in production.

RAG operations

Index refresh, drift monitoring, cost per query, latency budgets and feedback loops from real users.

How we deliver

A six-step model, from assessment to managed operations.

Every engagement follows the same rhythm — so business, IT and delivery stay aligned from opportunity to outcome.

Assess

Corpora, entitlements, question inventory, success criteria.

Design

Index topology, chunking strategy, retrieval and eval plan.

Build

Ingestion pipelines, hybrid retrieval, reranking, citations.

Evaluate

Golden set scoring, red-teaming, refusal and safety tests.

Launch

Canary rollout, monitoring, feedback capture.

Operate

Refresh, tuning, cost and latency optimisation.

Where it lands

Use cases already in production with enterprise clients.

Grounded support answers

Agent assist and self-service answers cited from policy and knowledge content, not model memory.

Policy & compliance search

Employees get answers with the exact clause, version and effective date attached.

Contract and claims intelligence

Retrieve obligations, exclusions and precedent across long-form documents at scale.

Field and technical knowledge

Manuals, schematics and prior resolutions surfaced at the point of work.

Runs on

Partner platforms we implement

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Industry patterns

Industries where this ships fastest

Healthcare Providers
Health Payers
Financial Services
Insurance
Retail & Ecommerce
Public Sector

See industry solutions →

Quick answer

What does an enterprise RAG implementation require?

Enterprise RAG requires a governed content pipeline, chunking and embedding tuned to the document type, permission-aware retrieval that respects source-system access, grounded generation with citations, and continuous evaluation on a labelled question set. Retrieval quality — not the model — determines whether answers are trusted.

Last reviewed 2026-08-05

Permission-aware retrieval is mandatory

Every chunk carries the access control of its source. Retrieval filters by the requesting user's entitlements so the assistant cannot surface a document the person could not open directly.

Citations make answers auditable

Each response links to the exact source passage used, which is what moves a RAG assistant from demo to approved production tool in regulated environments.

Evaluate retrieval separately from generation

We score recall and precision on a labelled question set before judging answer quality, because most 'hallucination' complaints are retrieval failures.

Enterprise RAG consulting

Enterprise RAG consulting starts with the corpus, not the model: source inventory, freshness and ownership, permission model, document quality and duplication. Most retrieval failures trace back to content and entitlements, so we fix those before tuning embeddings or prompts.

Source, freshness and ownership inventory
Permission model mapped into retrieval
Content quality and de-duplication plan

RAG implementation services

RAG implementation services build the pipeline end to end: connectors and incremental ingestion, layout-aware chunking, hybrid keyword and vector retrieval, re-ranking, citation rendering and caching — instrumented so every answer can be traced back to its source passages.

Incremental ingestion and connector maintenance
Hybrid retrieval with re-ranking
Traceable citations on every answer

Graph RAG implementation

Graph RAG implementation adds a knowledge graph over entities and relationships — products, policies, providers, contracts — so multi-hop questions resolve correctly. We use graph retrieval selectively, where relationship traversal genuinely beats vector similarity, and measure the difference.

Entity and relationship modelling
Multi-hop question resolution
Benchmarked against vector-only baselines

Enterprise RAG implementation and evaluation

Enterprise RAG implementation ships with an evaluation harness: golden question sets per domain, retrieval precision and recall, groundedness and citation accuracy, refusal behaviour and latency budgets — re-run on every content or model change.

Golden question sets per domain
Groundedness and citation accuracy scoring
Continuous re-evaluation on content change

Talk to us

Scope your enterprise RAG implementation

Bring your content sources and access model. We map retrieval design, permissions and the evaluation set that proves answer quality.

Source, chunking and retrieval design
Permission-aware access model
Answer-quality evaluation plan

Book a knowledge AI consultation See a grounded RAG demo Contact the team

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Where this fits in our practice

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Frequently asked

Questions buyers ask us first.

What is knowledge AI consulting?: Designing the retrieval, permission and evaluation layer that lets AI answer questions from your own content accurately, with citations and access controls intact.
What do RAG implementation services include?: Source connectors and ingestion, chunking strategy, embedding and index design, hybrid retrieval and re-ranking, entitlement enforcement, citation rendering and evaluation.
When is graph RAG implementation worth it?: When questions require multi-hop reasoning across related entities — policies to products, providers to contracts — and vector similarity alone returns plausible but incomplete context.
How do you stop RAG answers from hallucinating?: Grounded prompting with mandatory citations, retrieval confidence thresholds, refusal behaviour when evidence is weak, and groundedness scoring in the evaluation harness.
How do you enforce permissions in enterprise RAG?: Entitlements travel with the document through ingestion and are enforced at query time, so users only retrieve passages they are already authorized to read.

Next step

Book a working session with our enterprise rag team.

30 minutes. Your architecture, your data, your KPIs. You leave with a concrete pilot outline and a business case worth defending.

Schedule the session →Read the playbook