RAG / Chat with Your Data

Build governed ‘chat with your data’ solutions using Retrieval‑Augmented Generation (RAG) - secure retrieval, permissions-aware context, and measurable answer quality.

RAG (Retrieval‑Augmented Generation) is a pattern for improving generative AI answers by retrieving relevant information from your approved data sources and supplying it to the model as context. Microsoft provides reference architectures and guidance for implementing RAG-style applications with Azure services, enabling organisations to build ‘chat with your data’ experiences that are more accurate, more auditable, and better aligned to enterprise governance requirements than purely free-form prompting.

LW IT Solutions delivers RAG solutions that are production-ready and governed. We design the data boundary (what content is in scope), implement indexing and retrieval with appropriate permissions and security controls, and build an experience that supports real business workflows - search, summarisation, Q&A, and decision support. We also implement evaluation so answer quality can be measured and improved over time. The result is a trustworthy internal assistant that reduces time spent searching and reduces risk through controlled access and traceable outputs.

Talk through your requirements and leave with a clear next-step plan.

Book a discovery call

Service Overview

Highlights

Secure retrieval of approved content with permissions-aware access
Integration with multiple data sources including M365 connectors
Evaluation framework for measurable answer quality
Production-ready chat interface or API endpoints
Operational guidance including logging, monitoring, and runbooks

Business Benefits

Faster access to trusted, in-scope information for staff
Reduced repetitive queries to subject matter experts and support teams
Improved answer reliability with context retrieval and citations
Controlled AI responses through permissions and data boundaries
Scalable architecture for extending to new data sources and departments
Traceable outputs supporting audit and governance requirements

Typical use cases

Internal knowledge assistant for policies, procedures, and documentation
Project documentation Q&A for distributed teams
Self-service support for IT, HR, and operations queries
Summarisation of long-form content such as reports or research
Decision support by combining multiple internal data sources into a single response

Objectives & deliverables

What Success Looks Like

Enable staff to find answers faster using trusted, in-scope content (policies, procedures, knowledge bases, project documentation)
Reduce repetitive questions to SMEs and support teams by improving self-service knowledge access
Improve confidence in answers through retrieval, citations, and measurable quality signals
Control what the AI can and cannot answer by enforcing data boundaries and permissions
Create a scalable pattern that can be extended to additional data sources and departments

What You Get

RAG solution design pack: data boundary, retrieval approach, and security model
Implemented RAG pipeline (ingestion + indexing + retrieval + generation) for the agreed scope
Chat experience and/or API endpoints (as scoped) with governance controls
Evaluation pack: baseline tests, acceptance criteria, and regression approach
Operational pack: logging, monitoring guidance, runbooks, and ownership model

How It Works

Discovery - confirm use cases, data sources, constraints, and success measures.
Design - define the retrieval approach, security model, and evaluation plan.
Build - implement ingestion, indexing, retrieval, and generation pipeline; build the chat UI/API as scoped.
Validate - test quality, permissions boundaries, and failure paths; tune retrieval and prompts.
Operationalise - establish monitoring, runbooks, and change governance for content and prompts.
Handover - enable owners and agree a backlog for ongoing improvements and source expansion.

Engagement Options

Pilot Deployment - implement RAG solution for a single business unit to validate approach and measure impact
Full Deployment - scale RAG solution across multiple departments and data sources with governance controls
Advisory Session - review existing data, use cases, and RAG readiness; provide recommendations for implementation

Common Bundles

Customers who use this service often bundle with these services

AI Use Case Discovery & Value Assessment
Structured AI use case discovery to identify, score, and prioritise opportunities, producing a delivery-ready backlog with feasibility and value assessment.

Azure AI Foundry Enablement & Solutions
Stand up Azure AI Foundry to deliver governed, production-ready AI solutions with secure deployment, model evaluation, and operational handover.

AI Safety, Governance & Risk
Implement practical AI safety and governance with policies, approvals, logging, data boundaries, and controls that reduce operational and compliance risk.

Information Protection & Sensitivity Labels
Design and deploy Microsoft Purview sensitivity labels to classify data, apply protection controls, and support safer collaboration across Microsoft 365.

Records Management
Implement Microsoft Purview records management with record declarations, regulatory records, disposition reviews, and governance so retention decisions remain auditable and defensible.