Researchably’s Natural Language Processing technology allows their clients to search with extreme granularity and to answer complex ad-hoc queries within seconds. Then the AI technology monitors both old and new publications and learns the interests of their clients to notify updates regarding critical articles. Additionally, this AI can be programmed to send these articles to key stakeholders automatically. “Our value proposition to our clients is that we build our AI that can review scientific information much faster and with higher accuracy than their human teams,” says Cozzi, co-founder at Researchably.
The way the company interacts with the workflows of their client varies from one to another. Large pharmaceutical companies like Sanofi form the clientele of Researchably. The company’s product integrates with the client’s IT ecosystems, and a dedicated team ensures this integration is done correctly. “Our product lives within their ecosystem,” quips Cozzi.
Additionally, the company has access to all research papers that have been published and are public.
The three co-founders first started the company as a side project to create solutions for themselves. But soon they realized that this solution would have a greater impact on life sciences. To test their theory, they did consulting services for a few biotech startups. Eventually, by working with these organizations, they came out with their first product which was focused on academic researchers. It was through this experience that they found out that these products had the biggest value in pharmaceutical companies where the frequency and volume of searches were the highest. From this humble beginning, today they have grown and have reached a successful partnership with Sanofi. The esteemed partner has tested their pilot product and is currently negotiating a commercial contract.
“Our key motto inside the company is that we only hire people if we can help them grow,” says Cozzi. For this, Cozzi explains about their part-time team of data annotators who help in data categorization. This is usually considered to be a very low-pay, repetitive, tedious job. Cozzi states that the reason behind these annotators continuing to work for the company is because their workflow allows the staff to learn while still on their job. Additionally, the company is particular about the minute details of their solutions. For this, even the designers in the company are asked to learn about the biomedical sciences though it might not be their expertise so that the user experience gets greatly improved.
The team is now getting ready to raise investment to scale up their team to support more clients. “We started with big pharmaceuticals, as they have the biggest pain, and now we look to start working with mid-sized customers, and scale a little bit faster,” concludes Cozzi.