How Gretel found product-market fit: Ali Golshan on unlocking synthetic data for developers
In this episode of the Startup Field Guide podcast, Sandhya Hegde and Weil Lien Dang chat with Ali Golshan, CEO and co-founder of Gretel about the company's path to product-market fit. Gretel is a synthetic data platform that allows developers to generate artificial data sets with the same characteristics as real data so they can test AI models without compromising sensitive customer information or privacy. Gretel has a community of over 75,000 developers working with accurate, synthetic data.
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TL;DR
- ‍The founding insight: Gretel started as a side project around privacy that Ali worked on with co-founders Alex and John while he was still at Stackrox. The founders wanted to solve the problem of the cold start problem of data and the bottleneck of getting access to data, which developers suffer from the most.
- ‍Core ICP: The founders wanted to focus on developers because they suffer the most from the cold start problem of data, which takes up roughly a third of every project.
- ‍Iterating to product-market fit: The market pull was more around the utility and accuracy of the output of their models versus privacy, and they ended up orienting their messaging around synthetic data.
- ‍Early design partners: Gretel raised a seed round from Greylock and worked on productizing an API for synthetic data, collaborating with Google on privacy, and validating assumptions about user preferences for building blocks vs. prescriptive workflows.
- ‍Gretel’s AI strategy: Gretel’s founders accelerated their investment in multimodal synthetic data platforms and built a framework called Gretel GPT, which has become a valuable tool for customers.
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