Foxglove launches data search tools to help robotics teams find critical information faster
Foxglove released new capabilities to help robotics teams locate and organize mission-critical events within growing volumes of operational data. The updates include Data Search and Curation, Bring Your Own Storage (BYOS) deployment, and a free Basic Seat tier for expanded team access.
The shift in robotics development has moved from generating more data to finding the most essential data quickly. Teams need to debug issues, investigate failures, and improve system performance without wading through unnecessary logs.
Finding the critical data
Data Search allows users to query multimodal robotics data directly, identifying events of interest without preprocessing or moving data into a separate warehouse. The curation capabilities let teams tag, annotate, and enrich events to preserve findings and build training datasets.
Adrian Macneil, Foxglove's co-founder and CEO, said the real challenge is "finding the critical 1 percent that drives improvement in the real world." Teams that improve iteration speed typically have stronger data workflows connecting production robot data to better decisions and faster model improvements.
For safety-critical environments, faster access to relevant data accelerates debugging and model training, strengthening what Macneil called the "data flywheel" that helps teams learn and improve systems over time.
Expanding access across teams
The new free Basic Seat tier extends visibility beyond engineering to QA, triage, safety review, and management teams. More stakeholders get direct access to the same robot behavior and system performance data that engineers use.
This reduces handoff friction between development and operations, accelerating issue investigation and improving alignment as robotics programs grow.
Enterprise data control
BYOS lets enterprise teams host multimodal log data in their own cloud object storage while Foxglove provides managed compute for indexing, query, search, and evaluation workflows. The model addresses stricter data residency and infrastructure requirements without the operational burden of running self-hosted Kubernetes deployments.
The updates reflect Foxglove's shift from observability tooling toward a platform that captures and learns from all types of multimodal data across the physical AI data lifecycle.
For IT and development teams building robotics infrastructure, understanding data analysis workflows and AI for IT & Development practices becomes critical as deployment complexity increases.
Your membership also unlocks:
