When trying to figure out what to do after an extensive career at Google, Motorola, and Flipkart, Punit Soni decided to spend a lot of time sitting in doctors’ offices to figure out what to do next.
It was there that Soni said he figured out one of the most annoying pain points for doctors in any office: writing down notes and documentation. That’s why he decided to start Suki — previously Robin AI — to create a way for doctors to simply start talking aloud to take notes when working with patients, rather than having to put everything into a medical record system, or even writing those notes down by hand. That seemed like the lowest hanging fruit, offering an opportunity to make it easier for doctors that see dozens of patients to make their lives significantly easier, he said.
“We decided we had found a powerful constituency who were burning out because of just documentation,” Soni said. “They have underlying EMR systems that are much older in design. The solution aligns with the commoditization of voice and machine learning. If you put it all together, if we can build a system for doctors and allow doctors to use it in a relatively easy way, they’ll use it to document all the interactions they do with patients. If you have access to all data right from a horse’s mouth, you can use that to solve all the other problems on the health stack.”
The company said it has raised a $15 million funding round led by Venrock, with First Round, Social+Capital, Nat Turner of Flatiron Health, Marc Benioff, and other individual Googlers and angels. Venrock also previously led a $5 million seed financing round, bringing the company’s total funding to around $20 million. It’s also changing its name from Robin AI to Suki, though the reason is actually a pretty simple one: “Suki” is a better wake word for a voice assistant than “Robin” because odds are there’s someone named Robin in the office.
The challenge for a company like Suki is not actually the voice recognition part. Indeed, that’s why Soni said they are actually starting a company like this today: voice recognition is commoditized. Trying to start a company like Suki four years ago would have meant having to build that kind of technology from scratch, but thanks to incredible advances in machine learning over just the past few years, startups can quickly move on to the core business problems they hope to solve rather than focusing on early technical challenges.
Instead, Suki’s problem is one of understanding language. It has to ingest everything that a doctor is saying, parse it, and figure out what goes where in a patient’s documentation. That problem is even more complex because each doctor has a different way of documenting their work with a patient, meaning it has to take extra care in building a system that can scale to any number of doctors. As with any company, the more data it collects over time, the better those results get — and the more defensible the business becomes, because it can be the best product.
Read the source article at TechCrunch.