AI answers grounded in your knowledge
RAG development services — retrieval-augmented generation for enterprise search, RAG chatbots and AI knowledge assistants, grounded in your documents, data and systems of record, with citations.
Quick answerRAG (retrieval-augmented generation) development builds AI systems that fetch relevant content from your documents, data and systems of record at query time and use it to ground the model’s answer — with citations. It covers ingestion and chunking, embeddings and vector search, hybrid and graph retrieval, grounding and evaluation, and production deployment.
RAG, from pipeline to product
Each links to a dedicated page.
RAG Chatbot Development Services
A conversational assistant that answers from your content with citations — for support, employee help or product Q&A.
ExploreAI Knowledge Assistant Development
An internal assistant that searches, synthesises and drafts across your sources, permission-aware.
ExploreHybrid & graph retrieval
Keyword + vector + graph retrieval and re-ranking for accuracy where relationships matter.
ExplorePermission-aware retrieval
Filtered to each user's entitlements, mirroring your source-system access model.
ExploreRAG evaluation & tuning
Groundedness and helpfulness scoring, and fixes for an existing RAG system that hallucinates.
ExploreEnterprise AI Services
A shared retrieval service that every AI use case can call — part of the enterprise AI platform.
ExploreWhat keeps a RAG system from making things up
Strong retrieval
Hybrid keyword-plus-vector search, re-ranking, and graph retrieval where relationships matter.
Good chunking & ingestion
Change detection, structure-aware chunking, and metadata that makes retrieval precise.
Grounded prompting
The model must answer from retrieved passages and cite them — and refuse when evidence is weak.
Permission filtering
Retrieval scoped to the requesting user's entitlements.
Evaluation harness
A versioned test set scoring groundedness and answer quality on every change.
Feedback loop
Thumbs-down and corrections become new evaluation cases and retrieval fixes.
Why RAG for enterprise knowledge
| Requirement | RAG | Fine-tuning |
|---|---|---|
| Keep answers current | Update content | Retrain the model |
| Cite the source | Yes | No |
| Respect per-user access | At query time | Not possible |
| Cost to update | Low | Training run |
| Best for | Changing enterprise knowledge | Style, format, latency |
RAG built to be trusted
GraphRAG experience
Context-aware retrieval that makes data relationships explicit for the model.
Permission-aware
Retrieval mirrors your source-system access model.
Evaluation-first
Groundedness scored on every change; no demo-and-hope.
Platforms behind the team
RLCatalyst and Spectra accelerate build and run.
Security & PII handling
Redaction and audit logging as standard.
Enterprise scale
350+ Data & AI specialists across regulated industries.
RAG, tuned by industry
Content sources, sensitivity and audit needs differ sharply by sector.
Financial services
Document AI, risk and fraud models, and governed copilots that meet audit and compliance needs.
Healthcare & life sciences
Clinical and research assistants, knowledge retrieval and automation with data governance built in.
Retail & consumer
Personalization, demand forecasting, and customer and associate assistants across channels.
Manufacturing & supply chain
Predictive maintenance, quality vision, and agentic workflows for planning and operations.
Public sector
Secure, private-LLM deployments for citizen services, case work and knowledge access.
Technology & ISVs
Embed generative AI features, agents and RAG into your product with a partner who ships.
- AWS
- DataStax
- Salesforce
- Snowflake
Scaling this across the enterprise?
Our Enterprise AI Services practice brings generative AI, agents and LLMs to production at scale — with the governance, platform and operating model to keep them reliable, compliant and cost-controlled.
RAG and retrieval insights from Relevance Lab
Rethinking enterprise analytics with GraphRAG and context-aware AI
Why data engineering stalls on missing context — and how GraphRAG makes data relationships explicit for AI.
Read More Case studyGoverned AI coding assistants with Research Gateway & Amazon Bedrock
Secure, self-service access to AI coding assistants with institutional control over data and usage cost.
Read More BlogLLM in a Box: generative AI inside the Trusted Research Environment
A pre-configured private AI appliance that brings LLMs to sensitive data without it leaving the environment.
Read MoreRAG Development Services: frequently asked questions
Content last reviewed: September 2026
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