The ground truth platform for AI.
Today's AI can interpret questions, synthesize context, and generate responses with remarkable fluency. What it can't do reliably is retrieve verified facts or compute numbers. Ask the same question twice and you may get two different answers, with no way to tell which one is right.
Kepler fixes this at the architecture level. We separate language understanding from data retrieval: AI handles intent and reasoning, while deterministic code fetches data and runs calculations. The result is outputs that are traceable, reproducible, and defensible.
Read our founding story →Trust is the bottleneck for AI adoption.
In finance, healthcare, legal, and government, decisions hinge on precise numbers. A wrong figure in a pitch book, a fabricated citation in a filing, a hallucinated data point in a diagnosis: the consequences are severe and immediate. These industries can't adopt AI until they can verify what it produces.
The standard approach is to make AI itself more accurate: better training data, smarter retrieval, tighter guardrails. That reduces the error rate. It doesn't reach zero.
Our approach is structural. Instead of asking AI to produce numbers, we prevent it from doing so entirely. A semantic layer maps every natural language concept to a precise, verified definition. AI interprets your question; code retrieves the answer. Every output traces back to its source.
Built by engineers who know the cost of bad data.
Vinoo Ganesh
Seven years at Palantir building data infrastructure for defense and intelligence, then Citadel's first Head of Business Engineering. A career spent on the hardest version of the same problem: making critical data systems trustworthy.
John McRaven
Eleven years at Palantir building the products that sat between questions and answers for the world's most demanding users. Deep expertise in data platforms, ontologies, and systems where correctness is non-negotiable.
A founding team built for this problem.
Our team includes senior engineers from Palantir, Citadel, Meta, and Stanford. Financial experts who have built $100M+ businesses. Product leaders who have shipped at scale. We've been building toward this, in different forms, for over a decade.
We're based in New York and growing. If this sounds like the kind of problem you want to work on, we're hiring.
Backed by builders who know what it takes.
Our investors built the foundational data platforms and AI labs that define the current era. They know what it takes to build infrastructure that earns trust at scale.
See it in action.
Request a demo to experience the difference between AI that is probably right and AI that is provably right.
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