Case Studies
Overview
Context
Challenge
Testimonial
Contribution
Impact
Why It Matters?
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Global Pharmaceuticals

Enterprise-Scale RAG & Multi-Agent AI

Overview
Context
Challenge
Testimonial
Contribution
Impact
Why It Matters?
Overflow
Overview & Metadata

Enterprise-Scale RAG & Multi-Agent AI Framework for a Global Pharmaceutical Leader

Accelerated delivery of an enterprise-grade Retrieval-Augmented Generation (RAG) and multi-agent AI system—establishing the foundation for organisation-wide information retrieval automation.
Client Context
Client Context

A global pharmaceutical company engaged Hybrid Mind to provide specialist AIengineering support within a wider vendor team.

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Their objective was to develop a scalable, reusable RAG and multi-agent framework capable of supporting multiple business units and millions of documents.

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Hybrid Mind Supported →

Our technical expertise directed and supported delivery of architecture, engineering standards, backlog alignment,and business engagement—strengthening delivery momentum and ensuring technical coherence across teams. 

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Challenge

Key Challenges We Faced

The programme faced several challenges:

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1. Fragmented documentation and slow onboarding processes.

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2. Bureaucratic access controls delaying development and knowledge base setup.

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3. Ambiguous specifications requiring continual clarification with business teams.

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4. Divergent development branches and inconsistent engineering practices.

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5. Risks caused by misuse of generative coding tools such as GitHub Copilot.

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These obstacles slowed progress and required disciplined technical leadership to stabilise, rationalise, and accelerate delivery.

Our Contribution

Intelligence Connected delivered:

1. Architectural Leadership

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  • Designed the streaming architecture, including language detection, confidence scoring, and backend–frontend integration.
  • Guided development of a reusable knowledge library and an early multi-agent, multi-RAG system.

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2. Engineering Quality Governance

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  • Introduced coding standards, PR discipline,linting, formatting, and best-practice engineering workflows.
  • Acted as gatekeeper for code quality and technical direction.

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3. Stakeholder Alignment

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  • Provided clear communication with business stakeholders, ensuring realistic expectations and clarified scope boundaries. 

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Alongside technical leadership, our engineer delivered direct,hands-on engineering work that materially advanced the platform:

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  • Built core backend components for the streaming RAG pipeline,including language detection, chunk scoring, confidence aggregation, retrieval orchestration.
  • Implemented key elements of the multi-agent system, defining agent roles, communication flows, and response/arbitration.
  • Refactored and stabilised code into modular, testable components,improving reliability and reducing duplication.
  • Delivered backend–frontendintegration work for real-time streaming,error handling, and improved user responsiveness.
  • Consolidated multiple prototypes into a single production-ready codebase,enabling consistent development and easier future scaling.
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Impact

Hybrid Mind works with enterprises to unlock real value from AI, from readiness to measurable impact

We combine strategic insight with hands-on technical execution to help enterprises turn AI potential into measurable, lasting business impact
  • Enhanced RAG performance, streaming capabilities, and retrieval reliability.

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  • Unified the codebase, eliminating divergent development branches and improving maintainability.  

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  • Enabled scalable reuse of the AI library across departments, supporting document sets reaching into the millions.

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  • Estimated 30–40% reduction in time to find relevant information and generate reports.  

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  • Recognised by the client for strong engineering leadership

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Why It Matters?

Why It Matters?

Thiswork forms the foundation for organisation-wide automation of search,retrieval, and knowledge workflows.

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It demonstrates Hybrid Mind’s strengths in:

  • Enterprise RAG and vector-search engineering
  • Multi-agent AI systems
  • Embedding highly effective specialists within complex multi-vendor ecosystems
  • Combining modern ML approaches with robust software engineering disciplines
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