Email and a smarter notebook might be enough for handling communication for projects or experiments inside a team in a lab in some university basement. But when you have around 200 scientists working on discovering something new — say, a new drug — that communication process is going to quickly break down, and Sajith Wickramasekara that sits somewhere between science and software.

That’s the goal for Benchling, which Wickramasekara hopes will make life easier for researchers and help simplify and speed up the process of scientific discovery. Specializing in life sciences, Benchling aims to create a comprehensive suite of tools that help researchers thoroughly log their processes and collaborate among other scientists. Benchling looks to provide a rigorous platform that can take a lot of the work away from researchers, who instead might be documenting everything in email, Excel sheets, or just in a notebook somewhere. Benchling said it has raised a $14.5 million round of financing led by Benchmark Capital, with participation from F-Prime Capital and Thrive Capital. Benchmark’s Eric Vishria is joining the company’s board of directors.

“I was always planning to go to grad school to become a scientist,” Wickramasekara said. “Obviously since I’m working here I took a kind of left turn. As someone who was doing both science and software, on the software side of things I felt like i had really great tools for working with other people, and on the science side I felt like there were really great scientific tools but not great tools for working with other people.”

At its core, Benchling is a suite of applications and tools that include ways to design experiments as well as document them during that process. Researchers can track materials they are producing, manage their physical inventory — like even tubes or containers — and helps scientists standardize and easily query information from existing or previous runs. The service seeks to capture all of this in some unified platform that a company can deploy across a whole fleet of researchers and teams. Wickramasekara says more than 100,000 scientists are using the platform.

Benchling was initially born as a sort of smart notebook for scientists and academics. While that’s where it got started — and where a lot of the learning happened — eventually the team ended up creating something a little more formalized that it could sell as an actual product. That step proved a little more challenging as academics tend to be either alone or in small teams, so they don’t necessarily need the robust tools that a product like Benchling might have when commercialized.

“The freeform nature of a lab notebook is actually sufficient [for academia],” Wickramasekara said. “In the industry, that’s where all the structure comes in. We have a team as part of our customer success and implementation, we help customers come up with the right model and complexity and adjust their business processes. At the end fo the day, all these customers do something slightly differently. But we work with probably more than 80 customers and 25 do antibody research, so we figure out all the best practices over time. We help customers think about the tradeoffs vs one data model for another.”

Benchling also offers those same employees a suite of auditing tools, which Wickramasekara would be critical as it looked to move into larger companies that are dealing with more sensitive IP. For a company looking to discover new drugs, keeping that process under tight control is especially important — especially when they are working with organizations like the FDA. Benchling admins get a comprehensive view of who is doing what within the system, as well as guidelines around documentation.

Part of the challenge will be catering to all the niches and needs these individual companies might have throughout their own unique experimentation processes. Each lab is different, with its own quirks, and Benchling aims to be a unified platform that covers as many scenarios as possible, even with help tuning and adjustable models. So that means that there is room for other tools that could tap other niches and becomes the one-size-fits-all. But over time and with enough data, a tool like Benchling could figure out not only the best practices for specific labs, but also ones they should use — and then cover all those bases.