Hilbert raises $28 million to push AI from analysis into action

Hilbert, an AI startup focused on automating growth decisions for enterprises, raised a $28 million Series A on April 15, 2026, led by Andreessen Horowitz. The company says its software connects data across teams, structures it for AI models and then recommends actions with an attached dollar value, placing it in a crowded but commercially attractive corner of the enterprise AI market.

Hilbert’s pitch is decision-making, not dashboards

The startup is positioning itself as a system that helps companies move from analysis to execution. Rather than simply surfacing insights, Hilbert says its platform pulls together internal data so models can suggest concrete actions and estimate the financial impact of those choices. Founder Nazli Tan framed the product as a way for companies to learn more deeply from their own operations.

That approach is aimed at a problem many enterprises still face: they have plenty of data, but too little automation around what to do with it. Hilbert argues that AI agents alone are not enough if they do not connect to the systems that drive revenue and customer behavior.

Customers and pricing point to an enterprise test

Hilbert says Walmart is already using the platform, and its customer list also includes FreshDirect, Blank Street and Levain. The company says pricing scales with company size and data volume, with contracts ranging from hundreds of thousands of dollars to millions.

Those figures suggest the business is being sold as enterprise software with meaningful implementation costs, not as a lightweight productivity add-on. For investors, that makes the company’s revenue model closer to core operational software than to experimental AI tooling.

Why this round stands out in the AI startup market

The funding comes at a moment when AI startups are still drawing large checks, but investors are increasingly asking where the operational return will come from. Hilbert’s answer is to tie model output directly to business actions, a framing that is easier for corporate buyers to justify than broad promises about generative AI.

Andreessen Horowitz’s lead role in the round also signals continued backing for startups that sit between data infrastructure and applied AI. In that layer of the market, the competition is less about model scale than about whether a product can be embedded into daily enterprise workflows and produce measurable results.

For Hilbert, the next challenge is proving that AI can reliably help companies decide what to do with their data, and that those decisions are worth paying for at enterprise scale.