<aside> <img src="/icons/chess-queen_gray.svg" alt="/icons/chess-queen_gray.svg" width="40px" /> Overview Public markets today are driven by algorithms. The largest hedge funds rely on quantitative models, with tools like Bloomberg supporting every decision. But 50 years ago, things were different; trading was based on charts and phone calls. This is where private markets stand now—dependent on data aggregators like PitchBook, Crunchbase, and Harmonic. We’re bringing an analytical layer to private markets.
Aviato holds more alternative datasets than anyone else, from employee vesting schedules to credit card revenue insights. And with this data, we build models and classifiers to make predictions, transforming raw data from aggregation into actionable analysis.
The problem is, if everyone has the same data, where does the “alpha” come from?
Our value-add is that we don’t just provide data; we also support a range of custom data science tasks, effectively acting as an internal data science team for our customers. The economics make sense: hiring an in-house data scientist would cost a firm $100K+ annually, and they wouldn’t have the data flexibility to work with. We offer a solution at half the cost, with reusable data science filters that can be applied across various markets. These data science tasks also increase our contract value, going beyond the offering of a platform alone. For example, we developed a custom filter for one of our clients, Eclipse VC, to identify individuals with unusual career progressions, and we can apply the same filter for recruiters as well.
A lot of people see us as building a “better PitchBook.” While that’s a decent business ($552M annual revenue in 2023 with 13.0% YoY growth), it’s not an exciting vision for me, and I don’t think it’s be best way to frame what we’re aiming to build at Aviato. See more: 🗺️ Vision
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What do we mean when we say we have better data?: Why internal data systems matter
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<aside> 💡 Founders