Our Mission
Solve optimization problems where humans are at the center
We build AI that puts people first. Instead of just making organizations more efficient, we ensure AI systems actually help humans thrive. Our approach combines advanced simulation, verification, and recursive optimization to build AI that organizations stake their reputations on.
We're building AI systems that organizations can depend on when mistakes aren't an option. Solving human-centric optimization problems requires diverse perspectives—we bring together curious minds from computer science, psychology, economics, healthcare, policy, and beyond to tackle the fundamental challenge of reliable AI in critical domains.
Founded by technologists from Google, Meta AI, Databricks, Coda, Canva, and Plaid, we've come together around a shared belief: AI in high-stakes domains must provide quantified confidence in its behavior, enabling organizations to make informed risk decisions about deployment.
Our team brings together diverse expertise—from quantitative research and large-scale infrastructure to product development and domain specialization—united by the challenge of building systems that organizations can truly depend on. Our interdisciplinary approach draws from economics, physics, biology, mathematics, and computer science.
Our Principles
What guides our work building reliable AI for critical domains.
Statistical confidence first
99% accuracy isn't good enough when lives are on the line. We build verification systems that handle both deterministic verification for math and physics, and verification for human biases and worldviews.
Complete transparency
Every AI decision must be traceable and auditable. Our systems provide quantified confidence intervals on behavior, not black box predictions.
Continuous improvement matters
Our AI systems continuously learn and adapt, using automated drift detection to improve performance while maintaining safety boundaries verified through simulation.
Evidence over promises
Every system proves itself through rigorous simulation testing before deployment, then improves through real-world feedback while maintaining verified safety boundaries.
Our Approach
The core principles that define how we build AI systems.
Human-centric optimization
We build systems that optimize around human outcomes, not just algorithmic performance. People and populations are at the center of every optimization problem we solve.
Statistical rigor
We believe in quantified confidence over intuition. Our systems provide statistical guarantees that enable organizations to make informed risk decisions.
Domain partnership
We amplify human expertise rather than replace it. Our technology empowers domain experts to achieve outcomes that neither could accomplish alone.
Recursive improvement
Our systems continuously learn and adapt from real-world feedback while maintaining verified safety boundaries. Improvement is built into the core architecture.
Backed by industry leaders
We've raised $6.5M from investors who understand that trusted AI is the next frontier
Their support enables us to think long-term while moving with the urgency this moment demands.
Join the team
We're building AI systems that organizations stake their reputations on. Whether you're a researcher, engineer, domain expert, or bring a completely different perspective—if you want to solve hard problems that matter to humanity, we should talk.
See Open Roles