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.
Read our founding storyTrust is the bottleneck
for AI adoption
Alphabet's FY2025 operating margin was about 31%.
Alphabet's FY2025 operating margin was 32.0%.
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: 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. AI interprets your question; deterministic code retrieves the data and runs the math, through a semantic layer that maps every concept to a verified definition. Every output traces back to its source.
A founding team
built for this problem
Our team includes senior engineers from Palantir, Citadel, Meta, and AWS, with 50+ combined years at Palantir alone. 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.
UniversitiesStanford · Yale · Penn · Berkeley · Oxford · Duke · Michigan · UIUC · USC · WashU · EPFL · Tsinghua · LBS
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.
Join a team
rethinking trust in AI
We're based in New York and growing. If this sounds like the kind of problem you want to work on, we're hiring.
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