Keewano Raises $12 Mn Seed to Build Database for AI Machine Reasoning at Scale

KeewanoDB records every action taken by users, devices, and AI agents. It keeps this information up to date, complete, organized by time, and with the right context. The database can analyze trillions of events at the same time and provide AI agents with concise results along with the context they need to make better decisions.

"We were trying to use AI agents to answer business questions like 'which of our users are on the same path as the ones who churned last month?'," said Keewano Co-founder and CEO, Mark Kardashov. "You could see what happened on any dashboard, but why it happened lived somewhere the databases couldn't reach. Our databases had been designed for humans querying tables, not machines that need to reason live, with complete context. To support agents properly, we realized we'd have to build a completely new storage format."

"Every wave of AI adoption exposes the layer underneath it that wasn't built for the new workload," said Judah Taub, Managing Partner at Hetz Ventures. "We think machine reasoning is exposing exactly that gap in analytics infrastructure, and Keewano is addressing it directly rather than patching around it. That's why we led the round."

"In 1964, Japan didn't try to make trains faster on the same track. They built a new line," said Kardashov. "Databases are at that fork now. AI agents need a fundamentally different data architecture, and we've built KeewanoDB as the new line."

Keewano was co-founded in 2024 by Mark Kardashov, Dima Karger, Pavel Bibergal and Vitaly Bukhovsky, KeewanoDB is a database built for AI agents and machine reasoning at scale. Traditional analytics databases are mainly designed for people who use tables, dashboards, and predefined reports to find answers. AI agents work differently, so KeewanoDB stores information in a way that is easier for machines to understand. It keeps every event complete, in the right order, with its context, and available as soon as it is recorded.

This design allows organizations to store large amounts of event data and lets AI agents process it quickly. Businesses can analyze long histories of events while reducing the storage and processing costs linked to AI-based analysis. KeewanoDB also does not charge customers based on the number of events, which the company says is possible because its database engine was built specifically for machine use.

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