Keewano is building next-generation data infrastructure for the AI era, purpose-built for machine-generated workloads at scale. Conventional analytics databases were designed for a world in which humans wrote queries against tabular data at human speed. Modern AI workloads operate under a different physics. Agents generate billions of events from millions of users, run continuously rather than on demand, and require a layer that can ingest, process, and reason across that volume without the cost and latency penalties of legacy stacks. Keewano's answer is KeewanoDB, an AI-first database built specifically for analytics, which the company benchmarks at 600x faster and 100x more storage-efficient than conventional alternatives, processing up to 256 million events per second while supporting multiple AI agents working in parallel.

On top of that infrastructure sits a multi-agent analytics platform that does the work product, analytics, marketing, engineering, and customer experience teams have historically had to do manually. Keewano's AI agents autonomously generate and validate hypotheses against large user populations, surface root causes through advanced time-series statistical models, and feed back distilled, actionable insights without manual queries or complex configuration. A behavior map layer visualizes user flows in real time, with built-in churn analysis and automated population segmentation. The result is a platform that lets every team across an organization act on user behavior with the speed and depth that AI workloads now demand, without an analyst in the loop for every question.