Israeli startup Edelbyte has raised $70 million in seed and Series A funding to develop a data analytics platform designed for AI agents. The company is targeting workloads in which agents need to search and analyze large, varied datasets, and says its system is intended to handle queries that are less predictable and more numerous than typical human-driven requests.
The funding round was led by Sequoia and Quiet Capital, with participation from Greenoaks and Swish Ventures. Edelbyte says it is developing the technology with four early customers in the Fortune 500 area, including deployments at petabyte scale. It did not identify those customers.
A unified engine for agent queries
Edelbyte describes its product as a single platform for search, analytical queries, vector and graph operations, and geospatial workloads. The company says a unified query engine federates queries across these capabilities, avoiding the need to assemble separate databases for each type of analysis. A built-in knowledge base is intended to supply context that can help agents locate relevant answers.
The startup says it is aiming for real-time performance below 100 milliseconds at a scale of hundreds of petabytes, while keeping ingestion costs at $0.01 per gigabyte with full indexes. These are company targets and pricing claims; the available information does not specify benchmark conditions or provide independently verified performance results.
Edelbyte says its software can run on premises, in public or sovereign cloud environments, or as a self-hosted deployment. That range of deployment options places the product across cloud and customer-controlled environments, although the company has not disclosed further details about supported cloud providers, architecture or availability.
Founders bring large-scale analytics experience
The company was founded by four people who previously worked on Azure Data Explorer, Microsoft’s managed big-data analytics service. Edelbyte identifies Avner Aharoni as CEO, Alexander Sloutsky as vice president of engineering, Evgeney Ryzhyk as CTO and Yoni Leibowitz as vice president of platform engineering.
Azure Data Explorer was developed under the internal name Kusto and uses the Kusto Query Language, or KQL. Microsoft’s service supports analysis of streaming, log, time-series and telemetry data. Edelbyte’s founders’ experience with that system is relevant to the startup’s focus on real-time analysis, but the company’s new platform is a separate product.
Competing in a crowded analytics market
Edelbyte expects to compete on speed and cost with established data and analytics providers including ClickHouse, Databricks, IBM, Microsoft, Oracle and Snowflake. Its argument is that an engine designed specifically for agent access can better accommodate the volume and unpredictability of agent queries than systems originally built around human users.
That position reflects an emerging challenge for analytics platforms: making organizational data accessible to software agents as well as people. Edelbyte’s approach combines multiple query types in one system and emphasizes low-latency access at very large scale. Whether it can meet its stated performance and cost goals in production remains to be demonstrated as its customer deployments develop.
The $70 million will support Edelbyte’s product development. The company has not disclosed a detailed timetable for general availability or named its early customers, leaving the four reported deployments as its initial evidence of demand.
