Traditional methods of research and development, specifically in material or chemical formulations, often consist of using multiple unspecialized software programs with disparate workflows and siloed information among researchers. San Francisco-based software company Uncountable, however, is trying to eliminate these pain points and create a uniform and collaborative workflow for researchers. “Scientists are typically setting up experiments in one program, then sending them out in another, then review, analysis, and data in another,” says Cofounder and Chief Revenue Officer Will Tashman. “We want to remove a lot of these handoffs, exchanges, and formatting issues that scientists typically run into and streamline everything.”
To accomplish this, Uncountable’s platform provides a single place to store, visualize, and analyze an R&D team’s or company’s experimental data. Utilizing this singular platform allows for greater research collaboration, direct experiment knowledge management, and faster development time for an organization’s product. Once historical experimental data is in the system, Uncountable’s artificial intelligence and machine learning algorithms help predict the performance of new ingredients, processes, and configurations when formulating a product. The AI platform can uncover performance relationships then make suggestions for production, while saving researchers anywhere from days to weeks to months of testing time.
Uncountable’s platform provides a single place to store, visualize, and analyze an R&D team’s or company’s experimental data.
The software is currently in use at large companies creating rubbers, adhesives, coatings, ceramics, consumer products, and more. Although each of these products have very different chemical properties and the companies themselves span unrelated industries, the problems faced by scientists in these labs are very similar. Each utilize complex systems of chemical ingredients, with up to 25 individual ingredients, and might require measurement of 30 or more attributes of the end product. All benefit from being able to readily synthesize product information. As it currently stands, Tashman says, “Big data doesn’t really exist in materials or chemicals.” Therefore, compiling this formulation data in one place not only eases the workflow now, but years into the future, while also cutting down research redundancies.
“When you’re developing a new material, whether it’s a glue or a tire or a paint, there are several steps in that process, even in the lab-scale. You need a system and platform that’s flexible,” emphasizes Tashman. Even in an established process, there can still be variables. Ingredients can come from different suppliers or batches, which can have slightly different attributes. “Those attributes are what scientists are using to think about hypotheses as to why this ingredient will help or hurt this product, so we need to be able to incorporate those,” continues Tashman. These multi-step processes and configurations are a complexity that Uncountable’s customers deal with day-to-day for their products.
The big question, however, remains: When there are so many variables within these products and multiple teams of researchers working on developing these materials, how can an organization practically begin streamlining this process? Tashman suggests starting small with one team: “We find a small team, maybe 10 or 15 scientists, that work in a relevant chemistry area and onboard that whole team into the Uncountable platform. This involves bringing in their previous. We then configure the platform to the complexities they need. We’ll also enable them to have run sheets that will allow the scientists to skip the steps of previous communication and hand work that they were doing before.” This approach allows the customer to see the full cycle of development and the benefits of the platform for a team to best determine use on a larger scale.
We want to show the mini-network of learnings, of collaborations, of speed-up, so that scientists, directors, VPs, CTOs, and CEOs can start to think about this as what happens in three years when everyone’s on the system, and there’s a continuous learning environment.
Tashman highlights the importance of starting with this small team to demonstrate the proof points, then scaling to slightly larger team(s), before converting to a full-fledged company-wide effort. This approach could take up to five years to transition a large, international company’s R&D, but enables well-established companies to ensure this streamlining works from lab-sized testing to production and across their otherwise siloed teams. “We want to show the mini-network of learnings, of collaborations, of speed-up, so that scientists, directors, VPs, CTOs, and CEOs can start to think about this as what happens in three years when everyone’s on the system, and there’s a continuous learning environment,” says Tashman.
This continuous learning enables Uncountable’s ultimate goal: to make the researcher’s life easier. Their customer focus grounds them to determine and solve each client’s unique pain points while simultaneously beginning a cultural shift of greater collaboration amongst R&D teams.
Working towards this goal and reaching these R&D teams has been enhanced with MIT Startup Exchange and Uncountable’s induction into the STEX25 accelerator program. “STEX25 has provided a lot of opportunities to present at large and small conferences in different chemistry areas and locations. They also have really strong connections to larger industries and companies that we want to work with,” Tashman outlines. “Coming with the namebrand and credibility of MIT, especially when many of these chemical leaders come from MIT or completed research here, adds a layer of credibility to us.” While the journey with prospective customers can be arduous, Uncountable has earned the trust of researchers and organizations to securely streamline R&D and enable faster innovation.