Synthetic Sciences

Foundation Models for Scientific Research

We’re building infrastructure for scientific superintelligence. We develop high-fidelity evaluations and RL environments for long-horizon scientific research, alongside post-training systems and reinforcement-learning methods designed for domains where verification is slow, expensive, and imperfect. We’re starting with ML research, building agents that can form hypotheses, run experiments, learn from failures, and make meaningful progress over long horizons.

Category

Design

Pricing

freemium

Difficulty

beginner

Y Combinator batch: Winter 2026

Directory records are starting points for research. Check the official website for current features, pricing and availability. A catalog sync is not a hands-on product review.

Official website