A great tool for shortening the innovation cycle, doing end-to-end value-driven development, and allowing organizations to fail often and fail early

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Four and a half years ago I started on a quest along with my co-founders to commercialize speech emotion recognition and behavioral tracking technologies. Having just transitioned from the CEO to the CTO role at Behavioral Signals in January 2019, following our second round of funding, our technical team was faced with the task of making our technology work not only in the lab but also “in the wild”, i.e., under adverse recording conditions, diverse use-cases, and languages, as well as, limited computing resources. Our team of machine learning engineers consisted at the time of eight people both junior and…

You are a deep tech startup. You are bringing some new capability to market, i.e., at least some of the outputs that your API provides are new to people, or significantly improving the performance/scale/scope of an existing capability. Basically you have some jaw dropping tech. But it is not quite there yet. You are missing the tons of (annotated) data needed to train your machine learning models, the 80% of the effort that is required to make your tech really work (not just in the lab but also in the wild) and the customer traction that will validate the relevance…

Part I: Hiring and Growing your Team

Photo by Guy Kawasaki on Unsplash

All these late nights working on the demo of your MVP have finally paid off. A VC or angel investor has offered your company a term sheet for a seed round. You and your CEO are sharing half a bottle of champagne and talking about next steps. Money is not in the bank yet but you feel bold enough to start voicing a plan for building your tech team. “It is time to substitute [some of] the off-shore team with some local talent” you say. Your CEO nods ambiguously mentally counting dollars.


Alex Potamianos

CTO and co-founder of Behavioral Signals

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