Secure, compliant RAG ecosystem for global teams.
Multi-agent Retrieval-Augmented Generation platform on Azure — replacing employees pasting confidential documents into external AI tools with a sovereign, audit-ready alternative.
The client is a global pharmaceutical company producing scientific content, medical communication materials, and marketing assets across multiple therapy areas. Their teams rely on extensive research documents, clinical summaries, and large SharePoint repositories for daily work. With fast-moving deliverables and distributed teams, the organisation needed a more reliable and secure way to access internal knowledge.
Teams frequently used external AI tools to summarise documents, draft notes, or prepare materials — resulting in inconsistent outputs, compliance concerns, and significant data-exposure risk. The organisation sought a unified internal solution that enabled secure access to SharePoint content, consistent LLM outputs, and complete governance over how AI was used.
A secure multi-agent RAG platform was developed on Azure to help employees retrieve insights from their SharePoint documents, access approved external references when needed, and generate consistent outputs aligned with internal guidelines.
The platform uses coordinated AI agents that retrieve information from SharePoint, consult approved external sources when explicitly requested, and update content where permitted. All processing and generation occur within the organisation's Azure environment, with standardised system prompts ensuring consistency and full observability capturing every retrieval step, agent action, and response for audit and governance.
- ▸Teams pasted confidential documents into external AI tools to summarise and draft, creating data-exposure risk.
- ▸Inconsistent messaging across teams as different employees used different tools, prompts, and references.
- ▸No visibility for leaders into what information employees accessed or how AI tools were being used.
- ▸Manual search across large SharePoint repositories consumed significant time before any actual writing happened.
- ▸Compliance teams could not assure regulators that AI-generated outputs aligned with scientific and regulatory guidelines.
What was built and how it fits together.
Multi-agent architecture
Specialised agents handle retrieval from SharePoint, web access when allowed, and content updates. A coordinator agent orchestrates interactions to ensure accuracy and compliance.
Automated document understanding
GPU-powered container apps process documents, extract tables and structured content, and transform them into searchable insights.
Project-level access control
Retrieval respects the organisation's SharePoint folder structure — users only access content they are authorised to view.
Consistent, compliant responses
Standardised prompts enforce scientific accuracy, brand-aligned tone, and regulatory messaging across all outputs.
Complete observability
Every AI action — retrieval, agent tool use, external referencing, final generation — is logged for compliance review.
Fully secure Azure deployment
All data ingestion, indexing, and LLM execution run entirely within the organisation's Azure environment.
The operational result, measured against the starting state.
- ▸Secure, compliant access to SharePoint documents without external tools.
- ▸Consistent, high-quality responses aligned with internal scientific and regulatory standards.
- ▸Reduced manual effort in searching and summarising content.
- ▸Strengthened governance through project-based access control.
- ▸Full observability for audits and compliance reviews.
- ▸Intelligent, tool-aware interaction — retrieval, reference, or update as needed.