VEA Systems delivers an end-to-end AWS cloud platform for automated drone-based cell tower inspection. AI-powered defect detection, structured findings, and a real-time dashboard replace costly manual climbs and inconsistent visual audits.
Custom ML models for antennas, cables, and structural anomalies.
Image upload triggers full inspection analysis with no manual steps.
Amazon Bedrock foundation model for advanced image reasoning.
React dashboard with annotated findings, severity levels, and site maps.
From site registration to follow-up actions, every step is tracked, automated, and auditable.
Register tower sites, upload RFDS design documents, and define expected antenna and equipment configurations per sector.
Network engineers coordinate flight paths, schedule drone pilots, and confirm site access for each inspection run.
Pilots capture high-resolution imagery (12MP+) across all tower sectors. EXIF data and GPS coordinates are embedded automatically.
Images upload via secure transfer. EventBridge fires immediately, kicking off the inspection pipeline.
Microservices validate, run custom vision models, compare detections against site config, and persist structured findings to the database.
Field technicians review annotated images and severity-ranked findings in the React dashboard. Bounding boxes highlight every detected issue.
Findings drive maintenance tickets, re-inspection scheduling, and network remediation. All actions are logged against the site record.
Two complementary AI layers cover both trained defect classification and open-ended visual reasoning.
Custom-trained computer vision models detect specific defect types with bounding box precision. The active model is managed in the database and hot-swappable without a redeploy.
Nova Vision runs as a second analysis pass, applying open-ended visual reasoning across full tower images and video frames. Catches anomalies that fall outside trained defect categories.
Findings from real Circet USA tower inspections across Philadelphia-area sites on Ericsson L600 infrastructure.
Fewer antennas or RRUs detected than the RFDS specifies. Signals active equipment is offline, removed, or obscured. Flagged as high severity and surfaced immediately in the dashboard.
More equipment found than the expected configuration. May indicate unauthorized additions, legacy hardware not removed, or misidentification. Logged for engineer review.
Loose bundling, unsecured cable trays, and dense cable tangles identified at Norriton Sector 80 and Sector 320. Left unaddressed these create RF interference and maintenance risk.
Active bird nest detected at Norriton lower platform. Obstructions block antenna apertures and cause signal degradation. Drone imagery enables safe identification without a tower climb.
Sites confirmed as matching their RFDS design specs are logged as compliant. Apollo 3-sector lattice tower and all three sectors verified against expected panel antenna counts.
Point cloud processing via AWS Fargate generates 3D tower models for structural anomaly detection and depth-aware equipment mapping, with NVIDIA Omniverse visualization support.
Fully serverless inspection pipeline built on AWS. Scales to any number of sites at approximately $80/month for a proof-of-concept footprint.
Image upload triggers the inspection pipeline automatically via event-driven routing across purpose-built storage areas.
Orchestrates the 4-stage inspection chain: validate, detect, compare, persist. Each step is independently retryable with full logging.
Findings stored with severity and use-case indexes for fast dashboard queries. Also manages active model version, training state, and site metadata.
Feature development, staging, and production environments across dedicated cloud accounts. Git-based CI/CD promotes builds from feature development through to production.
In production for Circet USA, one of North America's largest telecom infrastructure services companies.
Production system live for Circet USA. Inspecting Ericsson L600 infrastructure across the Philadelphia market area with authenticated access.
Ongoing development and integration testing in a dedicated cloud environment. New AI capabilities and dashboard features are validated here before production promotion.
Built with React 18, TypeScript, Vite, Tailwind CSS, and shadcn/ui. Site maps, annotated image viewers, finding severity filters, RFDS document management, and ML training workflows.
Ready to replace manual tower climbs with AI-powered drone inspection? Tell us about your sites and inspection requirements.