Introducing Kepler

The Problem

High-stakes industries are falling behind on AI adoption. Their workflows can’t afford wrong answers. And AI can’t be trusted to give right ones because of hallucinations. The barrier isn’t that the models aren’t smart enough. It’s that no one can verify what they produce. The fix isn’t a better model, it’s a trust layer: every output traceable, every calculation auditable, every answer reproducible.

What Kepler Is

Kepler is the agent harness - the infrastructure layer that wraps around AI models to make their outputs reliable, traceable, and verifiable. The model is a replaceable component. The harness is the product.

In Kepler's architecture, the LLM orchestrates - it decides what data to gather, what to compute, how to structure the output. But every actual data point, every extracted value, every calculation flows through deterministic code pipelines. The LLM never touches the data itself. Every value carries provenance metadata back to its exact source. Every computation is auditable and reproducible. Verification loops cross-check outputs before users ever see them.

We started in finance because the stakes are highest and the tolerance for error is zero. We’ve built a finance research product that lets analysts supercharge their workflow: pulling comparables, building models and researching filings. No more double-checking every number AI spits out. Every number tracing back to the source, every time.

But the architecture - provenance, deterministic computation, verification - applies anywhere trust in AI output matters: chemicals, legal, healthcare. Models are commoditizing fast. The trust layer is what's missing and the market is massive.

The Team

The founding team spent a combined 40+ years at Palantir building the type of large-scale data infrastructure that Kepler requires. Our founding engineers led Foundry's core systems - Ontology, Fusion, Workshop, FoundryML, created Palantir's first AI platform and scaled data products at Meta to 1B+ users.

We’ve paired this deep technical foundation with a repeat founder profile. Our CEO built and scaled a data company to $15M ARR before successfully selling it. He then became Citadel's first Head of Business Engineering, experiencing first hand the problems we are now solving. We have a team who’ve been on both sides: building systems like this at massive scale and selling it into the buyers who need it most.

We’re backed by investors who built the modern AI and data stacks, plus the builders of iconic commercial businesses. This includes founders of OpenAI, Meta AI Research, MotherDuck, dbt Labs and Square as well as PebbleBed, Company Ventures and Mantis VC firms.

The Role

What You'll Own

You'll be the technical architect of our most strategic client relationships. You'll embed directly with our lighthouse customers, sophisticated financial firms and research organizations, to deploy, customize, and extend Kepler for their specific workflows. This is the engineering role that lives our core principle, Forward-Deployed with Product DNA: you own client outcomes while building the platform that serves the entire market.

This role is for engineers who want to work where the product meets the market, where your code directly shapes million-dollar research outcomes.

In the first few weeks you might:

Sit with a Portfolio Manager at an institution with $10B+ AUM to scope a workflow, then go build it.

Ship a vertical app on top of our core platform for a design partner and watch their analysts use it the same week.

Design an integration with a client's research stack (Bloomberg, CapIQ, proprietary systems) with provenance preserved end to end.

Take a gap you found in the field and turn it into a core product feature.

In the longer term, you'll own whole customer engagements and verticals end-to-end, rotating between forward deployed work and core platform development so field insights land directly in our architecture. In a small team, there's nobody to hand things off to.

Forward Deployed Engineer