InnoScout AI: AI Platform for Drone Inspection
Intelligent image analysis – directly on-site on edge hardware. Offline-capable, GDPR-compliant, no cloud required.
What is InnoScout AI?
InnoScout AI is an AI platform that adds intelligent analysis capabilities to drone inspections. The software runs directly on-site on edge hardware (NVIDIA Jetson) – no cloud, no internet. Cloud and hybrid options are on the roadmap.
For whom: Drone service providers who offer professional inspections and want to automate their analysis.
Our approach: We don't develop drones. We make existing drones intelligent.
Flexible Architecture
One software, three deployment options. Choose based on your requirements for data privacy, connectivity, and accuracy.
Edge
100% local – Data never leaves the site
Hardware on-site:
NVIDIA Jetson (ground station)
Drone integration on roadmap
Features:
- Analysis directly after the flight – on-site
- Works completely offline
- Maximum data sovereignty
- GDPR-compliant by design
Ideal for: Critical infrastructure, sensitive sites, areas without network coverage
Hybrid
Local + Cloud – Best of both worlds
Data flow:
Pre-filtering local → Detailed analysis cloud
Features:
- Real-time feedback (local)
- Maximum accuracy (cloud)
- Only relevant data to cloud
- Reduced bandwidth
Ideal for: Large projects, detailed reports, gradual adoption
Cloud
100% Cloud – No hardware investment
Data flow:
Upload after flight → Server analysis
Features:
- No hardware required on-site
- Unlimited computing capacity
- Large data volumes
- No real-time feedback
Ideal for: Beginners, occasional use, post-processing
Comparison at a Glance
| Criterion | Edge | Hybrid | Cloud |
|---|---|---|---|
| Data leaves site | Never | Partially | Yes, completely |
| Internet required | No | For cloud part | Yes |
| Real-time feedback | Yes | Yes | No |
| Hardware on-site | Jetson | Jetson | None |
| GDPR compliance | Maximum | Configurable | Cloud-dependent |
Features
Thermal Defect Detection
AI-powered analysis of thermal images using YOLO Object Detection.
- • Automatic hotspot localization
- • Defect classification per IEC 62446-3
- • Cross-modal fusion with RGB (in development)
Hardware-agnostic
Works with standard drone image formats:
- • DJI Mavic 3T (verified)
- • Standard R-JPEG and RGB formats
- • More platforms on the roadmap
Cross-Modal Fusion
Combined analysis of thermal and RGB images (in development).
- • Fewer false alarms through cross-validation
- • Automatic thermal ↔ RGB matching
- • Higher detection accuracy
Automated Reports
Standardized inspection reports are automatically generated.
- • Overview map with defects
- • Detailed images (RGB + Thermal)
- • Classification by severity
- • Export as PDF
Use Case: Solar Inspection
The Problem
Photovoltaic systems lose up to 25% of their yield due to undetected defects. Manual inspection is time-consuming and misses systematic errors.
Target Defect Types (IEC 62446-3):
| Defect Type | Detection Method | Status |
|---|---|---|
| Hotspots | Thermal analysis | In Training |
| Bypass Diodes | Thermal anomalies | In Training |
| String Failures | Pattern comparison | In Training |
| Soiling | RGB + Thermal | Planned |
| Cell Cracks | RGB analysis | Planned |
Key Facts
Edge Deployment
Runs on NVIDIA Jetson – fully offline, no cloud upload
IEC 62446-3 Reports
Automatic PDF generation per international standard
Pilot Phase Q2 2026
AI models in training — pilot partners wanted
Roadmap
Current: Solar inspection – pilot projects starting Q2 2026
Planned:
| Phase 1 | Solar inspection | 2026 |
| Phase 2 | Wind energy | 2027 |
| Phase 3 | Infrastructure | 2027+ |
Become a Pilot Partner
What we're looking for: Drone service providers who want to test the platform in real-world operations.
What you get:
- Early access to the technology
- Direct influence on features
- Preferential terms after market launch