Unify multimodal data, AI and computational modeling across scientific discovery and development to empower faster, smarter decisions and accelerate time-to-market.
The pace of innovation is accelerating faster than most organizations can respond — but speed without precision is waste. The pressure is mounting with rising product complexity, exploding data volumes and growing market scrutiny. Incremental fixes to isolated tools and manual, trial-and-error workflows will not keep pace. The real risk is not competitors — it is a scientific operating model that slows progress and limits the impact of AI.
The organizations that win will be those that combine deep scientific understanding with the infrastructure to act on it faster, more consistently and at scale. That means establishing a connected, intelligent scientific operating environment with full traceability from design to decision — where every experiment becomes reusable knowledge, and AI and simulation work together to bring groundbreaking innovations to market faster.
R&D spending is rising — but returns are not keeping pace. Global pharma R&D spending grew 80% between 2016 and 2024, yet the infrastructure to turn that investment into faster decisions has not kept up.
57% of scientists cite data silos as the biggest barrier to using lab data effectively — leaving critical insight trapped and AI unable to deliver reliable results.
Only 22% of life sciences leaders have successfully scaled AI, and just 9% report significant returns — highlighting the urgent need for a structured scientific data foundation.
How can you turn disconnected scientific data into reusable, AI-ready knowledge that accelerates decisions and drives pipeline progress?
To accelerate innovation, organizations must shift from isolated systems to a connected, intelligent scientific operating environment — one that enables AI-powered, simulation-driven, multimodal discovery with full traceability from design to decision.
A connected scientific discovery and development environment structures scientific knowledge at the source, enabling AI and simulation to operate on high-quality, reusable data that compounds in value over time.
Eliminate manual handoffs, advance collaboration and make data AI-ready — ensuring every experiment becomes a reusable scientific asset that adheres to FAIR (Findable, Accessible, Interoperable, Reusable) principles.
Leverage a digital twin in early development. Simulate molecular interactions and predict formulation behavior to reduce experimental burden, expand design space and derisk candidate selection for stronger first-in-human outcomes.
Apply AI to context-rich experimental data to surface patterns sooner, explain outcomes and guide next-best experiments. Connect enterprise data through knowledge graphs so teams act on comprehensive insight — not fragments.
Unify experimental, computational and analytical workflows within a connected, traceable operating environment. Give cross-discipline teams shared, real-time views of multimodal data and analytics for confident go/no-go decisions.
Bridge the gap between scientific insight and commercial production with a connected, intelligent operating environment.
Unify scientific context across the R&D lifecycle. Connect experimental data, documents and results within a single multimodal framework that preserves relationships across entities, samples, assays and instruments.
Go from raw data to scientific meaning. Apply AI to experimental data, results and supporting context to surface patterns, explain outcomes and generate interpretable insights — accelerating understanding.
Enable end-to-end lab orchestration that coordinates instruments, workflows, data movement and decisions in real time — ensuring experiments are executed consistently and scientific workflows scale reliably.
Bring together experimental data, analytics and scientific context into intuitive, at-a-glance views — enabling scientists and leaders to make confident go/no-go and prioritization decisions across discovery and development.
Enable scientists to visually build and adapt workflows across modalities with low-code automation that connects instruments, registration, analytics and AI.
Enable FAIR data practices by capturing standardized metadata, rich context and complete lineage across systems — transforming experimental data into trusted, machine-actionable assets that can be reused for analytics and AI.