Industrial R&D is facing a reckoning. Innovation cycles remain painfully slow, due in no small part to a reliance on fragmented systems like spreadsheets and lab notebooks. This is despite $2.5 trillion spent globally on research and development annually. Uncountable, a cloud-based platform built for scientific R&D, is helping enterprises unify their experimental data, accelerate innovation cycles, and finally bring AI into the lab.
Founded in 2016 by MIT alumni Noel Hollingsworth and Will Tashman, along with Stanford’s Jason Hirshman, Uncountable has become a vital infrastructure layer for over 120 enterprise R&D teams, from battery makers and cosmetics giants to food tech startups and pharmaceutical developers. This month, Uncountable raised a $27 million Series A round led by Sageview Capital, signaling its next phase of growth, and a validation of its mission:
“Our goal internally is, ‘Can we make R&D more efficient by a factor of 10?’” Hollingsworth told MIT News. “Can we imagine a world where instead of getting the Tesla battery that’s going to come out in 2032, you get it next year?”
Hollingsworth and Tashman met as basketball teammates at MIT, where they developed their startup ambitions. Hollingsworth, who went on to work in sports analytics, and Hirshman, a ML specialist from Stanford, joined forces with Tashman, a materials scientist and former Apple engineer, to tackle a challenge too often overlooked: the broken state of scientific data management.
While enterprise functions like sales and marketing had dozens of tools automating workflows and surfacing insights, R&D departments were stuck in manual drudgery. “Despite how important [industrials] are, there weren’t many companies trying to help R&D,” Hollingsworth said in a IRI podcast interview. “We thought there could really be a fit for their needs.”
Their initial push, AI-first optimization, was met with a jarring realization. “We’d go into big companies expecting clean data, and what we got were Excel spreadsheets,” Hollingsworth admitted. “So we pivoted. We realized before we could offer AI, we had to solve the data problem.”
From Digital Paperwork to Data-Driven Discovery
Today, Uncountable offers a full-stack informatics platform that replaces patchwork systems, like ELNs (Electronic Lab Notebooks), LIMS (Laboratory Information Management Systems), PLM (Product Lifecycle Management), and QMS (Quality Management Systems) with a unified, AI-ready environment. The system allows teams to centralize all experimental data, standardize processes, and create a replicable framework for innovation.
Uncountable’s platform maps the entire scientific process into a structured, searchable system, offering a level of clarity and traceability rare in complex industrial research. And thanks to its deep integration with machine learning models, Uncountable learns from the data it captures, offering predictive insights and optimization recommendations at every step.
Growth Without Compromise
The company’s latest $27 million raise is a bet on the future of industrial R&D. The round will help Uncountable expand its ML capabilities, grow its presence in quality and regulatory-driven environments, and scale customer support.
Sageview Principal Caitlin Vorlicek, who now joins Uncountable’s board, sees a massive market in transition. “Uncountable is addressing a significant upgrade cycle in R&D departments, where the ability to leverage AI depends on addressing the existing lack of unified data systems across laboratories,” she said. “They’ve solved not only the data challenge but also the AI capabilities that modern R&D departments need.”
The company’s platform plays an especially strategic role in highly regulated, data-intensive sectors where compliance, reproducibility, and transparency are mission-critical. Its structured approach to data capture makes regulatory audits and cross-team collaboration significantly easier.
“There’s various studies showing the crazy number of experiments and trials that are redone because of poor documentation or poor sharing,” said co-founder Tashman. “We want to eliminate that waste and make R&D more focused, efficient, and innovative.”
Building a Better Future Through Better Data
The founders often point to the societal impact of accelerated R&D (faster vaccine development, more sustainable materials, and breakthroughs in clean energy) as proof of what’s possible when scientists are empowered by good tools.
“The future isn’t discovered; it’s invented by these hard-working scientists,” Hollingsworth told MIT News. “Getting to help all these partners, not just in one field, but every field, has been really amazing.”
Yet the company’s future is grounded in practicality. Hollingsworth is clear-eyed about the challenges of deploying AI in scientific settings: trust, replicability, and the need for crawl-walk-run adoption. “You get one chance with scientists,” he told IRI. “If it doesn’t work, they won’t come back to try again. That’s why we focus on small wins, clear goals, and data quality first.”
