ClearSpot
AI platform that combines drone photos and on-site sensors to automatically spot problems — like a failing solar panel or a manufacturing defect — without a person having to inspect everything by hand.
🔗 Visit ClearSpot
Description
Inspecting a large solar farm or a factory floor for problems the old way means someone physically walking the site or manually reviewing hours of drone footage, looking for a hotspot or defect that might be one panel out of thousands. ClearSpot automates that search: its AI looks at drone and sensor data on-site and flags exactly what's wrong and where, so a technician gets sent straight to the actual problem instead of searching for it.
ClearSpot ingests real-time SCADA, drone and field data, automatically plans and schedules drone inspection flights, analyzes thermal and RGB imagery for AI-driven fault detection (string degradation, hotspots, inverter losses), prioritizes issues by their estimated revenue impact, and automatically creates work orders routed into a maintenance system. It's aimed at solar asset operators, independent power producers, and industrial/manufacturing quality-assurance teams who need to monitor large physical sites without inspecting every square foot manually.
💬 Our review
The short version: ClearSpot's pitch — using drone + sensor data to find physical problems automatically across large sites — solves a genuine, expensive problem for solar asset operators, but the specific performance numbers it publishes deserve extra skepticism, since the company's own blog explicitly notes some published use cases are illustrative rather than real customer results.
Ranking detected issues by estimated revenue impact (rather than just listing every anomaly equally) is a smart, practical feature — a solar operator managing a large portfolio needs to know which fault is costing the most money right now, not just get a long undifferentiated list of defects. The honest caveat here is unusually direct: ClearSpot's own site states that "use cases presented are for demonstration purposes" with images "sourced from open databases and Google," which means specific figures like "98.4% fleet availability" or "€312K revenue protected" shown as examples should not be read as verified results from an actual customer — this is a young company (founded 2023) still building out its public case-study evidence, and worth confirming any specific ROI claim directly with a live reference customer during evaluation rather than trusting the marketing site's numbers at face value.
💰 Pricing
📊 Global score
🤖 AI-enriched data
No public pricing; segmented by customer type (Utility/IPP, C&I, O&M, EPC), quote-only.
Pros
Combines drone imagery, sensor data and SCADA in one automated inspection pipeline
Prioritizes detected faults by estimated revenue impact, not just a flat defect list
Automated work-order creation routed into existing maintenance systems
Purpose-built for large-site monitoring (solar farms, manufacturing) at scale
Cons
Company's own site discloses that some published use cases/metrics are illustrative demos, not verified customer results
Young company (founded 2023) with limited independently confirmed track record
No public pricing, fully custom quotes