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Pharma & Life Sciences

AI for life-sciences experts.

Scientists and analysts outgrow an everyday assistant fast. Give them a governed place to build their own apps call internal data, pick the right model, ship to production without rebuilding the platform each time.

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Deployed in Production at.

A major global pharmaceutical group

A major global pharmaceutical group built its internal AI application development platform on Dify Enterprise, following a multi-region IT evaluation.

The platform serves as the company's expert-tier AI environment, running alongside Microsoft Copilot with a clear handoff between the two: Copilot handles everyday tasks, and the moment someone outgrows it — say, they need to make an HTTP request — they move up to the expert environment.

By early 2026, it had 1,000+ active expert users with 40 models to choose from. Its internal AI academy had trained 4,000 employees, on the way to a 15,000-person target.

Read the full storyHow a Global Pharma Group Built Its Expert AI Layer on Dify Enterprise
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  • 1,000+

    Active expert users on the platform (Dify)

  • 40

    Models to pick from, per use case

  • 4,000

    Employees trained to use the platform

  • 15,000

    24-month onboarding target

Source: Dify Enterprise deployment at a major global pharmaceutical group, 2026

“Microsoft Copilot is for the beginners inside the company. Dify is for the experts. When a beginner's needs go beyond what Copilot can do — for example when they need an HTTP request — they get upgraded to the expert side.”
— the group's IT lead for enterprise data & platforms

Why
this industry, why now.

The constraints

Pharma and life sciences companies operate inside a specific set of constraints. R&D cycles are long and the underlying data is highly sensitive — clinical trial results, compound data, regulatory submissions — which limits what can go to a public AI service.

Regulatory requirements shape which models can be used and what outputs are auditable.

And the user population is not homogeneous: research scientists, data analysts, regulatory affairs teams and general office workers all have different AI needs, and a single general-purpose tool does not cover all of them well.

The role of AI

For this industry, AI is not another company-wide chatbot. The question is how expert users get access to AI that can handle what they work on, while the general workforce gets a standard assistant and the whole thing stays under one governance model.

Patterns we see.

Expert-tier internal AI development
Research IT, data scientists and regulatory analysts outgrow a general assistant quickly. On Dify, these teams build their own applications — calling internal data sources, selecting the right model for each task, deploying to production. The platform provides the runtime, governance and integrations. Expert teams provide the application logic.
Model-agnostic orchestration
Pharma use cases vary widely. Unstructured research text, structured clinical tables, biomedical literature, multi-modal lab data all call for different models. Dify does not lock to a single provider. Teams pick the model that fits the task and switch as better options become available, without rebuilding their workflows.
A tiered AI experience
Everyone across the company gets a general-purpose assistant for everyday work. Research and specialist teams get access to the Dify environment where they can build and run their own applications against internal data. IT manages one platform that serves both tiers, with access controls separating what each group can reach.

Discuss Pharma & Life Sciences workflows with Dify.

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