Query.AI

Continues to expand connector coverage (Splunk, Snowflake, BigQuery, S3, Microsoft Security) and has published a steady stream of product content, consistent with an actively developed platform, though no major version-release cadence is publicly tracked.

The federated-search approach avoids costly data centralization, but as a roughly 45-person company its platform has not been independently benchmarked at large enterprise data volumes.

A strategic investment from Cisco Investments specifically for its security federated search platform is a meaningful independent momentum signal beyond the company's own claims, on top of an SYN Ventures-led Series A round.

Federated search-without-centralization is a genuine architectural alternative to the dominant 'ingest everything into a SIEM/data lake' pattern, addressing a real cost and latency problem, though it competes with data mesh and security data pipeline approaches from other vendors.

Cisco's investment provides some independent validation of the technical approach, but there is no public third-party benchmark, named large-customer deployment, or MITRE-style evaluation confirming query performance or completeness at scale.

Rising SIEM and data-lake ingestion costs make federated, non-centralized search directly relevant to security teams under budget pressure, and the Splunk/Microsoft/cloud-native connector list matches common enterprise security data stacks.