Not demos. Not pilots. Our agents run inside top-three institutions managing over $1T in assets — and we move from pilot to production in weeks.
Finance's long tail of edge cases can't be solved with better instructions. Every hard problem we crack becomes infrastructure everyone benefits from.
We collapse the silo between finance and engineering by design. The person who knows why a regulation exists works side-by-side with the person who builds the constraint.
Every edge case you solve is immediately available across our entire platform. You are not just improving one client — you are advancing the whole ecosystem.
Advised by Dr. Fei-Fei Li and Lukasz Kaiser. Founded by alumni of JPMorgan, Goldman Sachs, Google, Silver Lake, and Amazon. You learn from people who shaped foundational AI.
We hire for divergent thinkers — people who fall in love with the problem, not a single solution. Your scope grows as the platform grows.
"The bottleneck is no longer people and hours — it's judgement, governance, and speed."
— Apoorv Saxena & Lak Lakshmanan, co-founders
Most AI succeeds in demos and fails in production. In finance, 95% accuracy on 50 aggregated inputs means 80% of final outputs are wrong. We build for the last mile — near-perfect task completion across the entire chain — because anything less erodes trust and kills adoption.
The distressed credit clause, the partial PIK payment, the cross-border transaction with conflicting regulatory jurisdictions — these are not bugs to be filed later. They are exactly why financial institutions exist. We treat the long tail as the product.
Every output must be traceable back to its source. Our Financial Compiler — a deterministic validation engine — checks agent outputs for integrity before a human ever sees the result. Accountability is architecture, not policy.
Getting an agent to correctly process a complex loan paydown requires a logic split, a data engineering fix, and an ML model working together — not a better prompt. We are divergent thinkers who layer incremental solutions until they compound into reliability.
When we solve an edge case for one client, the architectural improvement is available to all. Your underwriting criteria, risk appetite, and decision frameworks stay yours. You get the ecosystem's learning; you keep your alpha.
Agentic engineering is a new discipline. No one has a finished Financial Compiler or a complete library of every edge case. We embrace that uncertainty — and we compound our learning faster than any single institution can alone, because we see the aggregated complexity of the market.
We're hiring across engineering, product, and forward-deployed roles in New York.
