Flox Intelligence offers an AI‑driven edge sensor suite that captures visual, acoustic, and radar data to detect and classify wildlife in real time on transport corridors, airports, farms, and public venues. The system streams detections to a cloud platform that assigns risk scores, delivers low‑latency alerts, and provides predictive analytics, GIS dashboards, and compliance reporting through API, SMS, or dashboard integration.
Funding
Funding not disclosed
Founders
Product
Problem
Wildlife incursions on transport corridors, airport runways, farms, and public venues cause safety incidents, operational delays, and financial losses, while existing monitoring solutions are often manual, delayed, or lack species‑specific insight.
Solution
Flox Intelligence delivers an AI‑driven edge sensor suite (Flox Edge) that captures visual and acoustic signatures of animals in real time and runs on‑device inference to identify species and behavior. Detected events are streamed to the cloud‑hosted Flox Platform, where deep‑learning models decode intent (e.g., crossing, foraging) and assign risk scores. The platform generates low‑latency alerts and actionable recommendations that operators can receive via dashboard, SMS, or API integration with existing SCADA/EHS systems. Continuous data ingestion enables trend analytics, predictive modeling, and automated reporting for regulatory compliance. By combining edge computing with centralized analytics, the solution reduces response time, minimizes false alarms, and supports proactive wildlife‑conflict mitigation across critical infrastructure.
Target Audience
Primary customers are infrastructure operators—rail and highway authorities, airport wildlife management teams, large‑scale agricultural enterprises, and municipal agencies responsible for parks or golf courses—who require automated, real‑time wildlife monitoring and mitigation.
Features
- Rugged, weather‑sealed Flox Edge device equipped with high‑resolution cameras, ultrasonic microphones, and optional radar sensors for multi‑modal wildlife detection.
- On‑device GPU/TPU inference engine executing proprietary convolutional neural networks to classify species and behavior within 200 ms of capture.
- Secure OTA firmware updates and containerized AI models ensuring continuous improvement without field service visits.
- Cloud analytics pipeline that aggregates detections, applies Bayesian risk modeling, and produces predictive movement forecasts.
- Configurable alert engine delivering real‑time notifications via REST webhook, SMS, email, or integration with existing incident‑management platforms.
- Interactive web dashboard with GIS mapping, heat‑maps of activity hotspots, and exportable compliance reports (ISO 27001, GDPR‑compliant data handling).
- Open API (REST/GraphQL) for seamless embedding of detection data into transportation control systems, airport wildlife management tools, and farm IoT ecosystems.
- End‑to‑end encryption (TLS 1.3) and role‑based access control for data at rest and in transit.