Percepto Launches Axion Physical AI Platform for Multi-Source Infrastructure Inspection
Smart operations · Construction robots
Autonomous drone inspection provider Percepto launched Axion, a hardware-agnostic SaaS physical AI platform designed to aggregate and analyze inspection data from third-party drones, fixed sensors, and legacy archives across critical infrastructure.
Autonomous drone inspection provider Percepto announced the launch of Axion, a hardware-agnostic physical AI platform marking the company's strategic shift toward a software-first approach. According to Percepto, the SaaS-based platform enables operators of critical infrastructure—such as power transmission, upstream oil and gas, and downstream processing facilities—to ingest and analyze data collected from supported third-party drone platforms, mobile sensors, and existing inspection archives. The system utilizes over 160 AI models to interpret equipment conditions, detect anomalies such as downed wires or thermal irregularities in context, and cross-reference findings with historical records. Axion also incorporates large language model capabilities allowing users to query aggregated inspection records through plain-language prompts. Alongside the platform launch under new executive leadership, Percepto stated it will continue to manufacture and support its proprietary autonomous drone systems in the United States.
Key takeaways
- Percepto announced Axion, a hardware-agnostic SaaS physical AI platform that ingests data from third-party drones, fixed and mobile sensors, and existing archives.
- The platform features over 160 AI models for context-aware asset condition analysis, alongside LLM-powered natural language querying for inspection records.
- Axion targets critical infrastructure workflows, including power transmission and distribution, oil and gas, and downstream industrial facilities.
- Percepto confirmed it will continue producing and supporting its autonomous drone fleet, with manufacturing located in the U.S.
Source:The Robot Report Read the original ↗
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