Industrializing AI Pharma R&D
How a leading global pharmaceutical company replaced a fragmented stack of AI tools with one governed, self-service platform — automating the model lifecycle, scaling across cloud and on-prem, and giving every scientist a role-based multi-agent workspace for enterprise knowledge.
The Client
Our client is a leading global pharmaceutical company engaged in the research, development, manufacturing and delivery of innovative medicines for some of the world’s most complex and serious diseases. The organization operates across North America, Europe, Asia-Pacific and emerging markets, supported by a global network of research centres, manufacturing facilities and commercial operations.
Its teams span the entire pharmaceutical value chain — drug discovery, clinical development, regulatory approval, manufacturing and commercialization — and combine deep scientific expertise with real-world healthcare insight to deliver sustainable value to patients and health systems worldwide.
The Challenge
As the client’s AI and platform initiatives scaled, two pressures compounded each other. At the model layer, building, training and operating production-grade AI/ML at the scale demanded by global R&D required scarce data-science capacity, repeated tooling work, and ever-growing cloud and operational bills. At the user layer, information, models, applications and workflows had fragmented across multiple systems, environments and point tools — making it difficult for any persona, business or technical, to get a clean, role-relevant answer or to ship a new GenAI capability without weeks of approvals.
Three interlocking constraints were holding the enterprise back:
- Shrinking margins on AI/ML economics. Scaling models the traditional way meant near-linear growth in compute spend, data-engineering effort and platform support — pressure already showing up in cloud and operational bills as adoption widened.
- Fragmented tooling and information. Data, documents and operational signals were spread across S3, NFS, Dominolabs, ABCD, Orbit, ELNs, structured databases and internal apps. Business users lacked self-service access to contextual information, and developers lost time to manual searches and tooling dependencies.
- Permission and environment bottlenecks. Experimenting with new models, deploying applications and building agentic or automated workflows meant navigating several disconnected platforms, each with its own permissions, onboarding path and environment constraints. Innovation moved at the speed of approval cycles.
Leadership wanted a single, governed home for AI: one platform that could automate the AI/ML lifecycle, deploy intelligently across cloud and on-prem, and give every persona — business, data science, developer — a self-service multi-agent workspace tuned to their role and intent.
Our Approach
We partnered with the client’s R&D and platform teams to design and build AI on AMP — a unified, self-service AI platform that replaces fragmented point tools with a single, governed layer for the full AI lifecycle. The work was delivered through an iterative, use-case-driven engagement: a platform-first architecture, model-agnostic and deployment-agnostic, rolled out incrementally and validated continuously against real business and developer workflows.
The goal was deliberately broader than another GenAI tool. AI on AMP had to industrialize how AI gets built, deployed and consumed across the enterprise — bringing AutoML, hybrid scaling, agentic orchestration and role-aware information retrieval together in one place, without forcing the R&D community to abandon the tools they already trusted.
Inside the Solution
1. A Governed Sandbox for AI/ML Experimentation
At the heart of the platform is a controlled experimentation layer that lets users test and compare multiple AI/ML and GenAI models within a governed environment — accelerating prototyping without touching production workloads. An intuitive low-code interface lets teams configure experiments with a few clicks, reuse complex data-standardization pipelines, and connect directly to S3, NFS, Dominolabs, ABCD, Orbit and other client systems. Model explainability and data/model drift tracking are built in from day one, so innovation stays auditable.
2. Industrialized AI/ML Automation
AI on AMP automates the heavy lifting of the model lifecycle. Resilient, compliance-certified Intelligent Automation pipelines bring advanced AI/ML to scientists at global scale, eliminating repetitive build-and-deploy work. State-of-the-art AutoML capabilities — including Neural Architecture Search and reinforcement-learning-driven optimization, paired with model catalogues, hubs and dashboards — give teams a no-code path through the full ML lifecycle, from problem framing to monitored production.
3. Open-Source Model Onboarding, Without Lock-In
Rather than anchor the client to any single vendor, we built a flexible onboarding layer for selected open-source GenAI and lite-LLM models. Teams choose the model best suited to each use case and run it through the same governed pipeline used for proprietary models — preserving optionality as the open-source landscape evolves.
4. Hybrid Deployment — Cloud and On-Prem, by Design
The platform integrates with AWS SageMaker for scalable, managed deployment in the cloud, and extends natively to on-premise environments for workloads with data residency, compliance or infrastructure constraints. Adaptive hybrid patterns bring compute to where the data lives — or move data to compute — depending on which is more efficient. That includes processing petabyte-scale data from on-prem imaging microscopes and ELN databases, and enabling state-of-the-art computing paradigms such as quantum computing where applicable.
5. An “AI for AI” Optimization Layer
We embedded an optimization layer that uses AI to make AI cheaper and faster. Neural-architecture graph optimization, hardware-specific software tuning and multi-tenant execution have, in client benchmarks, delivered over 100% efficiency gains on training and inference workloads — directly reducing operational cost as adoption scales.
6. A Role-Based Multi-Agent GenAI Workspace
A single role-aware front door routes every query through a multi-agent orchestration layer that invokes the right specialist agent — Ontology, Deep Research, Clinical Trial, Coding, Workflow — and reaches the right model and data source for the user’s role and intent. Retrieval-augmented grounding keeps responses accurate, current and source-cited; orchestration keeps complex, multi-step reasoning tasks tractable.
7. Client-Specific Adaptors for Industry Tools
The platform ships with owned, client-specific adaptors that connect to industry-leading tools the R&D community already depends on — including LiveDesign and 3Dx. Built with disciplined software engineering practices, these adaptors let JRD scientists layer day-to-day productivity enhancements onto existing tools with just a few clicks, and enable reuse of capabilities across future projects.
Serving Every Persona
Business Users
Business-focused agents deliver high-level insights, summaries and decision-support outputs without requiring technical expertise. Responses are tailored to business context — plain language, source-grounded, and tuned to each user’s role, permissions and intent — so teams can make faster, more confident decisions on everyday questions.
Developers and Data Scientists
A dedicated coding agent assists with code understanding, generation, debugging and technical guidance, with contextual access to internal systems, repositories and documentation. Developers can spin up workflows, deploy applications and test GenAI models freely through the same governed platform — turning days of setup and troubleshooting into minutes of configuration.
Governance, Security and Scale
- Cost and usage governance. Budget thresholds and real-time alerts notify project owners when workloads approach defined limits, keeping cloud and operational spend predictable as adoption scales.
- Structured intake. A standardized project intake process captures business use cases, ownership and approval checkpoints before any workload is onboarded to the platform.
- User-based access controls. Every agent, data source and model respects the user’s role and permissions, ensuring secure and compliant information retrieval.
- Built for extension. The architecture is designed to grow — new roles, agents, models and data sources can be added without rework, creating a durable foundation for enterprise-grade AI adoption.
Outcomes
AI on AMP moved the client from a fragmented toolset and manual processes to a centralized, self-service AI ecosystem. Early outcomes include:
- A single, governed home for AI/ML and GenAI — replacing multiple scattered software tools across cloud and on-premise environments.
- Material efficiency gains on AI workloads — over 100% improvement on training and inference benchmarks via the AI-for-AI optimization layer, directly reducing cloud spend per use case.
- Faster, role-relevant insights for business users — accelerating decision-making and reducing reliance on support teams for day-to-day information needs.
- Streamlined deployment and coding assistance for developers — improving productivity and significantly reducing time spent on setup, tool selection and troubleshooting.
- Confident, self-service experimentation — teams evaluate and adopt open-source GenAI models without waiting on extended approvals or environment bottlenecks.
- A scalable, governed foundation — ready to extend into additional domains, agents, data sources and computing paradigms as enterprise AI adoption matures.
Why It Worked
Three design choices defined the outcome. First, we treated this as a platform problem, not a model problem — solving for how people across business, data science and developer personas actually work, rather than optimizing any single use case. Second, we kept the architecture model-agnostic and deployment-agnostic, so the client could absorb open-source innovation at its own pace, across both cloud and on-prem, without being locked to a vendor. Third, we built governance, role-based routing and an AI-for-AI optimization layer in from day one — so self-service experimentation and adoption could scale with confidence rather than risk, even as compute demands grew.