We build autonomous research environments that generate, evaluate, and refine high-quality training signals for frontier AI systems.
Publicly available data is increasingly exhausted, noisy, and poorly aligned with the capabilities frontier of modern models.
As models surpass human baselines, producing useful demonstrations requires increasingly scarce and expensive expert workflows.
A system cannot reliably learn capabilities that humans themselves cannot evaluate, discover, or execute at scale.
Historical training paradigms have relied on human output. To reach Artificial Superintelligence, the training infrastructure itself must become autonomous.
Relying on the internet and scraped data.
Scaling intelligence through manual human annotation.
AI-assisted workflows relying on expensive domain experts.
Self-improving, agent-driven scientific research environments.
Prev: Microsoft Research and EPFL, Lausanne. Publications in top-tier AI venues (e.g., ACL, AAAI, and IJCAI).
Prev: Oracle and Microsoft Research. Gold Medalist from IIIT Hyderabad and ACM ICPC Regional competitor.
If you're building frontier models and struggling to generate reliable training signals for capabilities that are difficult to evaluate, we'd like to talk.
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