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Unlocking the future of
machine intelligence in
real-world environments

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.

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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.

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Get started with our solutions
and accelerate your autonomous
developments.

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.

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