Founding GTM

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 own how we get in the room with the investment teams we want to work with: who we meet, where we meet them, and how a first conversation turns into a working relationship. The people we work with are portfolio managers, CIOs, CTOs, and heads of data and AI at hedge funds, private equity firms, and allocators. Most of them don't answer cold email. The conversations that matter start with an introduction, a dinner, or ten minutes at a conference, and they go somewhere when the person across the table feels understood.

A lot of the job is teaching. Most firms are still working out what AI should do inside their walls, and many of their engineers are trying to build it themselves. You'll explain how our system works, why we split the work between code and the model, how it fits the data and tools a firm already has, and what deploying on their data actually involves. You'll often do that for a PM and a CTO in the same meeting, so you'll need to understand the technical side in real depth.

This is a senior individual contributor role with no quota or territory. You'll be the person a PM treats as a peer: someone who has sat in a seat like theirs, talks about their workflow in their language, and leaves a meeting with a sharper read on what they need than they gave. Our CEO stays close to our most important relationships. Your job is to make sure there are more of them, at the firms that matter most to us.

Some of this you'll do alongside other members of the team, but all of it is yours to elevate and make stronger.

First conversations. Discovery with new firms: how a team works today, where its data lives, what its engineers are already building, and what a first engagement would need to prove. You'll run these yourself and bring in our CEO and engineers when the conversation calls for it.

Conferences and events. Which ones we go to, who we meet before we arrive, and what happens after. Also the rooms we host ourselves: dinners and roundtables small enough that the right twelve people actually show up.

Shaping each deal. No two of our deals look alike. One firm wants our API under the agents its engineers are building, another wants analysts working in our app, another wants one workflow running on its own data before anything else. You'll work out which one you're looking at, and shape the pitch and the offer around it with our CEO and product team.

The buyer's voice in product and marketing. What PMs, CTOs, and heads of data tell you, turned into clear input for our product team and into how we describe Kepler: the story for each segment, the decks, the one-pagers. You'll use what we build the way a user would, and say so when it misses.

Keeping it all moving. Inbound requests, live conversations, follow-ups, NDAs, scoping sessions, and materials, across dozens of relationships at once and over months. You'll own the coordination, so every conversation has a clear next step and nothing goes quiet.

Relationships and partnerships. The people in your network and ours who should know Kepler, and the path to each of them: customers, investors, advisors, and the people they'd introduce us to. Also the firms that sit next to our buyers every day, like prime brokers, fund administrators, and data providers, that could bring us into conversations we can't reach on our own.

Market read. Which firms are adopting AI and how, what competitors are telling the same buyers, and which segments we should go after next.

In the first few weeks you might:

Learn our system well enough to walk a CTO through it, from where their data lives to how every number in an answer gets cited.

Take over our inbound and live conversations, and make sure each one has a clear next step.

Map the people you and the team already know at the firms we want to work with, and pick the twenty conversations worth having first.

Choose the next conference worth our time, set up the meetings before we arrive, and follow up on every one.

Host a dinner for a dozen people who run research or data at investment firms, and turn a few of those seats into second conversations.

Sit in on a live deal and tell our product team the three things the buyer would push back on, and how you'd change the pitch.

In the longer term, you'll build how we grow our relationships with investment firms: the events, the partner channels, the advisors, how we run a deal from first meeting to signature, and, when the volume calls for it, the team. You're the first person here whose whole job is the market, so a lot of what the function becomes is going to be your call.

How We Work

We're a close team, working together in an office in New York (with Wagyu, our office dog). We use AI tools heavily: Claude, Cursor, whatever makes us faster. Fluency is assumed. Our engineers work directly with each customer on the customer's own data, and you'll often be in those rooms with them.

The pace is startup-fast but the bar is high. Our buyers have heard every pitch for AI in finance, and they remember the ones that didn't hold up. Our sales cycles are long: a deal often takes months of conversations, a scoping session, a security review, and an evaluation phase, with a PM, a CTO, and often someone above