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RAG Development

RAG Development Services

Build RAG systems that retrieve from approved knowledge, respect permissions, cite sources, evaluate retrieval quality, and help users verify answers instead of trusting unsupported model output.

Discuss RAGSee how we work

  1. 01 Prepare

    Prepare source content, metadata, chunking rules, permissions, refresh cadence, and ownership.

    part of the answer

  2. 02 Retrieve

    Design retrieval, reranking, citations, prompt behavior, and fallback responses around real questions.

  3. 03 Evaluate

    Evaluate groundedness, retrieval hit rate, coverage, latency, and failure cases before launch.

Scope

RAG systems for trusted knowledge

Enterprise RAG needs content design, metadata, permissions, citations, retrieval testing, and update workflows as much as it needs a vector store.

  1. Knowledge ingestion

    Process documents, websites, help centers, policies, databases, and internal knowledge.

What You Get

What your RAG engagement includes

A useful RAG system depends on source preparation, retrieval quality, citations, permissions, and ongoing content operations.

  • knowledge_audit.md

    Knowledge audit

    Sources, formats, sensitivity, ownership, update frequency, and answer boundaries reviewed first.

    PoliciesLegalmonthly
    Help centerSupportweekly
    Product docsProductper release

  • retrieval_pipeline

    Retrieval pipeline

    Chunking, metadata, hybrid search, reranking, filters, and prompt context designed around real questions.

    chunk     by heading, ~400 tokens
retrieve  hybrid, top 20
rerank    keep top 5

  • citations_ui

    Trust features

    Citations, source previews, fallback responses, and permission-aware access for safer answers.

    cite       every claim, inline [n]
unsure     say so, link the doc
no source  decline, offer a person

  • eval_set.jsonl

    Evaluation process

    Question sets, expected answers, retrieval checks, hallucination review, and improvement routines.

Process

Make knowledge usable by AI

We start with the questions the system must answer, then design the retrieval pipeline to support them.

  1. Step 01 of 05

    Use-case definition

    Clarify users, questions, answer boundaries, data sensitivity, and success metrics.

  2. Step 02 of 05

    Source preparation

    Clean, structure, tag, chunk, and index approved knowledge sources.

  3. Step 03 of 05

    Retrieval build

    Implement search, reranking, filters, citations, prompts, and response behavior.

  4. Step 04 of 05

    Evaluation

    Test against known questions, wrong-answer cases, permission scenarios, and latency targets.

  5. Step 05 of 05

    Operations

    Plan updates, monitoring, feedback, and governance for long-term usefulness.

Best Fit

For organizations with valuable knowledge locked in documents

Answer customer and agent questions from approved help content.

One knowledge base. 3 of 7 columns matter to Support teams; every answer points back to it.

SourceOwnerWho can seeUpdatedHit rateCited / wkGapsSensitivity
PoliciesLegalAll staffMonthly91%4122Medium
Help centerSupportPublicWeekly90%1,2805Low
Product docsProductStaffPer release86%3069Low
ContractsLegalLegal onlyOn changeNot indexed0NoneHigh

Illustrative rows

Proof

Why partner with Multivariate

We build RAG like a production information system: source control, permissions, retrieval quality, evaluation, and user experience all matter. Each engagement is shaped around practical buying criteria: integrations, governance, quality checks, handoff, and measurable operating outcomes.

  1. Exhibit A

    We design citations and verification paths.

  2. Exhibit B

    We test retrieval quality with real questions.

  3. Exhibit C

    We plan content update workflows.

FAQ

RAG development FAQs

Multivariate analytics team

Replies within 24 hours

What data can a RAG system use?

It can use approved documents, web pages, help centers, databases, policies, and other knowledge sources that can be ingested safely.

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