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Private BI and AI, built on the Go Fig platform

Go Fig turns an organization's systems into one governed model, runs business intelligence on it, and puts AI agents to work with a person approving. It runs in our cloud, the customer's cloud, their own servers, or air-gapped. Every deployment runs on our platform, sold as a subscription.

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Nathan Freystaetter

Founder & CEO · Capital One · Square

[email protected]

Every company bought AI but almost none can act on it

Private deployment is the wedge

Serious buyers want AI on their real data and will not send it out. Go Fig runs in our cloud, theirs, on their own servers, or air-gapped, so the same platform reaches organizations whose data cannot leave their control.

Agents are the product

Skills read the data, triggers wake the agent on a schedule or a signal, and actions wait for a person to approve. Internal work can run on its own once the team trusts it, and anything that reaches a customer always waits. That's the difference between software a company bought and work it stopped doing.

The data underneath compounds

Agents fail on the data, not the model. Go Fig brings a customer's systems into one place they own, with one set of numbers. Every connection and correction makes the agents better. Removing us means rebuilding the layer everything runs on.

Founder-market fit

Built by a data scientist who spent a decade on the enterprise data stack at Square, Capital One, and Oportun. Then he ran a small business and lived the other side of the problem. The thesis came from customer interviews, not a market map.

A $240B market in boutique data and AI infrastructure

Go Fig serves a $240B market: boutique data and AI infrastructure for small and midmarket companies. These companies want AI on their real data, but they cannot staff a data platform team and will not send that data to a public model.

It started in finance, but the problem was never finance

Go Fig began with twelve structured interviews with finance leaders, run at the end of 2025. Every one named the same problem: a dozen systems that didn't talk to each other and a person joining them by hand. That shape shows up in sales, service, and operations too. It was louder in finance because finance has a deadline. The wedge moved to sales, then to private deployment, because serious buyers want AI on their real data and will not send it out. The thesis didn't change. Capture the data first and the agents are worth building. The research

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