Our purpose
We believe that Scientific AI is the key to solving humanity’s grand challenges.
The world's hardest problems, in medicine, materials, food, and energy, are waiting on answers buried in scientific data no one can fully use.
We exist to set it free, to turn the world's scientific data into something AI can finally reason over.
That's the future we're building toward, and we built a company unlike any other to reach it.
That data holds the patterns behind the next cure and the next breakthrough, but it's scattered and locked away, out of reach for the AI that could find them.
When that data flows, science compounds: every experiment builds on the last, across teams, companies, and borders.
The hope and hype of Scientific AI
More compute, cheaper cloud, better models: the conditions for a leap in scientific discovery are real, and the expectations are enormous.
But AI doesn't get to those discoveries on enthusiasm. It gets there on data, and the data isn't ready. Until scientific data is engineered to a standard AI can use, at scale and under real compliance, most of what's promised stays a demo.
Wht scientific data resists AI
Four structural problems keep scientific data from reaching its potential. Any one of them slows a lab down. Together, they make Scientific AI nearly impossible to do at scale.
The data and use cases are genuinely complex
Scientific workflows and the data they generate take real expertise to interpret. Generic data tools don't have it, so the meaning gets lost on the way in.
The ecosystem is fragmented
More than 500 vendors supply the instruments, lab software, and informatics applications in a typical R&D environment, each with its own formats and its own reasons to keep data inside its own walls.
The value chain is global
Tens of thousands of scientific organizations work with thousands of CROs and CDMOs. Every handoff between them is a chance for data to lose structure, context, or integrity.
The work is still DIY and done by hand
Most labs still treat scientific data as a series of one-off projects, disconnected from the scientific questions the data is supposed to answer. That approach doesn't scale, and it leaves most of the data's value on the floor.
TetraScience: Sui Generis
Our founders recognized that a sui generis approach — both technical and commercial — was required to enable the design and industrialization of scientific AI-native data.
To enable the assembly of large-scale and liquid scientific data sets, and to engineer sophisticated scientific data models comprising taxonomies and ontologies that are actionable by AI in furtherance of real-world scientific use cases, TetraScience has taken one-of-a-kind, full-stack approach.
Our approach
Science informs all we do
Each layer of the Tetra Scientific Data and AI Platform is explicitly and optimally designed for scientific data.
Expertise in all aspects of scientific use cases and data, combined with parallel expertise in the modern data stack and AI, informs the design and development of TetraScience’s scientific data integration schemas, taxonomies, ontologies, partnerships, compliance standards, and AI models.
Embed Tetra Sciborgs
In addition to the scientific knowledge encoded in the TetraScience stack, our forward-deployed “Sciborgs” are embedded with customers and operate as one with their science and data science teams in pursuit of their scientific goals.
Tetra Sciborgs combine formal scientific expertise (e.g., Ph.D. in Molecular Biology; former biopharma bench scientist) with augmented data modeling training. They take the form of scientific data engineers and scientific business analysts.
Peer learnings and best practices
TetraScience combines its Sciborg engagements with a library of deployment-ready scientific use cases and supporting tools. This library includes access to customer peer learnings and benchmarking derived from its leadership position across the industry, in furtherance of accelerated and improved scientific discovery, development, and manufacturing.
Liberate and future proof your scientific data
TetraScience is the industry torchbearer for a vendor-agnostic data stack and data-only business model. Our only agenda is to liberate, harness, and future-proof the power of all our customers’ scientific data. All TetraScience resources—human, technical, and financial—are aligned against this objective.
Eliminate walled gardens and vendor lock-in
Conversely, scientific endpoint vendors (instruments, ELNs, informatics apps, robotics, IoT sensors, etc.) have vastly different competencies and agendas. They dabble in the modern data stack and issue press releases about AI but possess no core competencies or IP in either. They seek to use data-related offerings in an attempt to lock customers into proprietary walled gardens.
Enable data scale and liquidity for Scientific AI
Scientific endpoint vendors only have access to a small subset of the scientific data required to deliver on Scientific AI, whereas TetraScience, as the Switzerland of scientific data, secures access to the superset of our customer’s scientific data—a precondition for AI-native data.
Collaborate without data friction
Our customers’ replatformed and engineered scientific data—aka Tetra Data—is the atomic building block of Scientific AI and is designed to flow between and among customers and their CROs, CDMOs, and vendors, fueling unprecedented collaborative innovation.
Radically improve your data and AI outcomes
This unprecedented data liquidity fuels ever larger data scale, which leads to richer taxonomies, more robust ontologies, and higher fidelity AI-native data. Evermore liquid and improving AI-native data continuously improves Scientific AI models and outcomes.
Science informs all we do
Each layer of the Tetra Scientific Data and AI Cloud is explicitly and optimally designed for scientific data.
Expertise in all aspects of scientific use cases and data, combined with parallel expertise in the modern data stack and AI, informs the design and development of TetraScience’s scientific data integration schemas, taxonomies, ontologies, partnerships, compliance standards, and AI models.
Embed Tetra Sciborgs
In addition to the scientific knowledge encoded in the TetraScience stack, our forward-deployed “Sciborgs” are embedded with customers and operate as one with their science and data science teams in pursuit of their scientific goals.
Tetra Sciborgs combine formal scientific expertise (e.g., Ph.D. in Molecular Biology; former biopharma bench scientist) with augmented data modeling training. They take the form of scientific data engineers and scientific business analysts.
Peer learnings and best practices
TetraScience combines its Sciborg engagements with a library of deployment-ready scientific use cases and supporting tools. This library includes access to customer peer learnings and benchmarking derived from its leadership position across the industry, in furtherance of accelerated and improved scientific discovery, development, and manufacturing.
Liberate and future proof your scientific data
TetraScience is the industry torchbearer for a vendor-agnostic data stack and data-only business model. Our only agenda is to liberate, harness, and future-proof the power of all our customers’ scientific data. All TetraScience resources—human, technical, and financial—are aligned against this objective.
Eliminate walled gardens and vendor lock-in
Conversely, scientific endpoint vendors (instruments, ELNs, informatics apps, robotics, IoT sensors, etc.) have vastly different competencies and agendas. They dabble in the modern data stack and issue press releases about AI but possess no core competencies or IP in either. They seek to use data-related offerings in an attempt to lock customers into proprietary walled gardens.
Enable data scale and liquidity for Scientific AI
Scientific endpoint vendors only have access to a small subset of the scientific data required to deliver on Scientific AI, whereas TetraScience, as the Switzerland of scientific data, secures access to the superset of our customer’s scientific data—a precondition for AI-native data.
Collaborate without data friction
Our customers’ replatformed and engineered scientific data—aka Tetra Data—is the atomic building block of Scientific AI and is designed to flow between and among customers and their CROs, CDMOs, and vendors, fueling unprecedented collaborative innovation.
Radically improve your data and AI outcomes
This unprecedented data liquidity fuels ever larger data scale, which leads to richer taxonomies, more robust ontologies, and higher fidelity AI-native data. Evermore liquid and improving AI-native data continuously improves Scientific AI models and outcomes.
