Edesto offers a fleet observability platform for autonomous robot deployments, continuously ingesting data from firmware, perception, planning, and fleet management to automatically detect, cluster, and diagnose failures. Its AI‑assisted root‑cause analysis provides rapid incident context and regression monitoring, helping robotics teams reduce manual troubleshooting, shorten mean time to resolution, and keep engineers focused on development.
Funding
Funding not disclosed
Founders
Product
Problem
Robotics teams struggle to maintain visibility into large, distributed robot fleets, leading to time‑consuming log analysis, unclear failure origins, and prolonged incident resolution. As fleet size grows, blind spots and blame‑shifting between hardware, software, and perception layers increase operational costs and slow innovation.
Solution
Edesto provides a fleet observability platform that continuously ingests data from all robot subsystems—including firmware, perception, planning, and fleet management—and automatically detects and clusters failures. The system applies AI‑assisted root‑cause analysis to contextualize each incident with historical patterns and related events, delivering diagnoses within minutes. By surfacing recurring issue types and regression trends across the entire fleet, Edesto reduces manual troubleshooting, shortens mean time to resolution, and frees engineers to focus on product development.
Target Audience
Primary customers are robotics companies and engineering teams that operate medium to large fleets of autonomous robots in field deployments, including manufacturers of warehouse, delivery, and service robots.
Features
- Unified data pipeline that aggregates logs, telemetry, and sensor streams from heterogeneous robot stacks in real time
- Automated failure detection and clustering to identify similar incidents across multiple robots and sites
- AI‑driven root‑cause suggestions that combine event correlation, historical patterns, and contextual metadata
- Regression monitoring with alerts for drops in success rate, new issue types, and increased failure frequency
- Dashboard visualizations of fleet health metrics such as success rate, average run duration, and incident trends
- Integration hooks for existing fleet management and monitoring tools to enrich observability data