We build APIs for searching and analyzing Physical AI data at scale, so providers have insight into the quality of their data, to sell more at higher prices.
We enable data providers to get on-demand signals and metrics on the quality of their data, without managing infrastructure. Quality control is painstakingly manual, and metrics are rarely defined with enough rigor to be reproducible. Teams often fall back on hand-crafted heuristics, even when data comes from different operators, sessions, and conditions.
Today, we help our customers evaluate data quality without training a robotics model. We believe data should be studied with the same seriousness and methodology that researchers apply to models.
We come from top embodied AI and ECE programs at Columbia University and National University of Singapore. Our team has topped Stanford's LLM benchmarks, built infrastructure at Jane Street and Verkada, and is backed by Y Combinator and angel investors from Oracle, Google DeepMind, and OpenAI.
Join us
If this thesis resonates with you, we'd love to hear from you. Explore Pareto or connect with Brandon or Kingston.
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