Autonomy
Software modules and toolchain for autonomy
Our modular autonomy stack provides the building blocks for robust, real-time decision-making in the most demanding environments — from mining vehicles to industrial robots and mobile platforms.
Navigation
& Path Planning
Adaptive route optimisation algorithms tailored for dynamic and constrained environments.
Perception
& Localisation
Robust localisation and perception capabilities, enabling machines to understand and navigate complex spaces with high accuracy.
System Integration Tools
APIs, SDKs, and simulation tools that simplify integration with your platform, infrastructure, and control systems.
Deployment-Ready Architecture
Flexible deployment options for edge or hybrid computing, optimized for reliability and minimal latency.
Operational Intelligence
AI-enabled insights for intelligent systems
Tripleye delivers AI-powered operational intelligence to transform raw data into actionable decisions. Our systems continuously learn, adapt, and optimise operations based on real-world observations and performance feedback.
Process Optimisation
Machine learning algorithms that identify inefficiencies and continuously improve productivity across operations.
Predictive Diagnostics
Real-time system health monitoring and anomaly detection to reduce unplanned downtime and maintenance costs.
Behavioral Analytics
Understand how systems interact with their environment — and with each other — to enable smarter coordination and control.
Edge-Intelligence & Event Triggers
Intelligent event-based data flows that enable autonomous actions or operator alerts, even in low-connectivity environments.
Physical AI
Simulation and testing to improve frontier models
Our Physical AI platform blends high-fidelity simulation, real-world testing, and model refinement — enabling continuous improvement of frontier AI systems under physically realistic conditions.
Physics-Aware Training
Train perception and decision-making models with physics-grounded feedback loops for enhanced robustness.
Digital Twin Environments
Build simulated replicas of your operating environments to test AI behavior before deployment.
Model Evaluation & Benchmarking
Measure AI system performance under a variety of edge-case and stress-test conditions.
Feedback-Driven Model Refinement
Close the loop between simulated training and real-world feedback to drive superior generalisation and adaptation.
