Six years ago, my co-founder and I set out to fundamentally reimagine how science could be conducted in the age of AI.
We were not interested in retrofitting yesterday’s science with algorithms to generate incremental gains, but in applying first principles to design a de novo AI-native scientific operating system to produce step function improvements across the entire value chain.
At the time, our hypothesis was highly non-consensus. Some called it naive and even misguided.
Today, our first principles approach has been vindicated. In the days, weeks, and months ahead, TetraScience will begin to more publicly share what we have been quietly, deliberately, and relentlessly building as we replatform science for the era of AI.
In furtherance of this, I launched a new Substack that lays out our case for a better scientific future. I do hope you’ll follow me there.
Welcome to the Scientific AI revolution.
Why It Matters Now
Eroom’s Law vividly illustrates the industry’s existential diseconomies of scale – R&D productivity has been declining for decades.
In response, we have built TetraScience to arrest Eroom’s Law by delivering industry-wide platform economies of scale via the industrialization of AI-native scientific data and AI-enabled use cases across the value chain.
Everything we have developed has been purpose-built for the era of AI and has been designed to overcome each of the limitations of the prior paradigm.
Introducing the Scientific AI Lighthouse (SAIL) Program
Through TetraScience’s new SAIL program, visionary pharma companies gain early access to our full range of data and AI capabilities to accelerate drug discovery, reduce CMC cycle times, enable in silico modeling, and increase scientist productivity.
The program is designed to shorten pre-IND timelines, improve candidate quality, and enable biopharma organizations to bring more products to market at lower cost and risk.
For decades, pharmaceutical R&D productivity has been constrained by highly fragmented datasets, bespoke workflows, manual processes, manifestly unscalable project-based approaches, and self-defeating incentive structures.
The SAIL model introduces a fully integrated set of capabilities, purpose-built for the era of AI, which directly addresses these challenges. We’re calling these new capabilities the Four Industrial Pillars of Scientific AI.
Together with Takeda, our SAIL launch partner, we’re demonstrating what the era of Scientific AI looks like—and inviting others to join us.
The Four Industrial Pillars of Scientific AI
Scientific Data Foundry
Deconstructs scientific data—captured in proprietary vendor silos—into atomic units which are organized into AI-native schemas, taxonomies, and ontologies, and productized for reuse, continuous improvement, and federated sharing. This not only industrializes AI-native scientific data, but future-proofs biopharma data against vendor lock-in amid a rapidly evolving landscape of ELNs, LIMS, instruments, IoT, and robotics, while enhancing compliance and audit readiness.
Scientific Use Case Factory
Productizes and mass-produces AI-enabled use cases and workflows by combining AI-native data from the Foundry into standardized, repeatable, and configurable processes. Hundreds of common scientific use cases across the R&D and manufacturing value chain will be deployed as part of the SAIL program, then made broadly available to the biopharma industry.
Tetra AI
Provides semi-autonomous and fully autonomous agentic capabilities to assist scientists in navigating complex, multi-step processes across R&D. Tetra AI proactively identifies and delivers the most relevant data across diverse experiments, traverses broader chemical and biological spaces, reveals patterns that manual workflows miss, and synthesizes vast inputs in parallel to guide faster, more confident decisions.
Sciborgs
To ensure successful adoption and change management, TetraScience forward deploys squads of scientist-engineers—“Sciborgs”—who operate at the nexus of science, data, and AI. Sciborgs accelerate cultural and operational transformation by embedding with pharma teams, ensuring sustainable adoption of Scientific AI.
Combinatorial Effects and Compounding Outcomes
Together, these elements create a self-reinforcing value-creation loop: every dataset refined in the Foundry increases the fidelity of future workflows; every use case produced in the Factory feeds learning back into Tetra AI; and every new ontology compounds across workflows and domains. The result is a scientific innovation flywheel—more usage generates better data, which drives higher-quality insights, which in turn enables new, more powerful use cases.
What’s Next
Schedule a Foundry and Factory tour.
If you and your teams want to lead, let’s start there. Drop me a note, and I’d be delighted to personally take you on the tour!
Patrick Grady
Co-Founder & CEO, TetraScience