Sramana Mitra: I have one last question on the engineering stack. To what extent were you able to leverage existing models and components that are out there? To what extent did you have to kind of do things from scratch? Anthony Scodary: That’s a good question. In general, we try to build everything in Gridspace
Sramana Mitra: Let’s go back to your company building. You bootstrapped for a year and a half to product. Then you had started getting these paid pilots. Did you raise any money or is it fully bootstrapped all along?
Sramana Mitra: They want the problem solved, and if the problem is solved by a machine; that’s perfect. Here comes the sensitive question. What does this do to the workforce? What does this do to the bottom line? What are the human resource and financial metrics of your solutions in the call center?
Sramana Mitra: What are the results of these deployments? Anthony Scodary: For instance, we have one customer that calls when you’re discharged from hospitals, and they call thousands of patients a day. Some people spend twenty minutes talking with Grace. We build all our own language and speech synthesis models, so we’re able to make
Sramana Mitra: Tell me a bit about the structure of these. As you were revolving from these pilots to actual deployment, sounds like your platform was strengthening. Who was doing the application layer? Was a part of your team doing the application layer or the customer’s team doing the application layer?
Sramana Mitra: Let’s trace this a little bit more granularly. When did you launch the company? When did you quit your jobs, or did you not quit your jobs? Did you start it before quitting your jobs? Anthony Scodary: No, I quit my job in November 2012 and I think we started a day later.
Gridspace is a wonderful case study of a speech technology company on the bleeding edge of Machine Learning and Generative AI. You will learn how the founders managed to bootstrap to large paying customers and then raise strategic funding. You will also learn the nuances of how they used various Open Source components and existing ML
Sramana Mitra: Well, either it’s going to go there or your product is going to be able to handle this kind of dynamic pricing model check where you can determine at what price point can you still be profitable and can cater and if that’s viable ROI-wise for your customer? If you can do that dynamic