Our Projects

Real-world implementations of our AI-powered solutions

Agentic AI Projects

Autonomous intelligent systems driving transport innovation

Flagship Core Platform

Autonomous Transport Optimization Network

Foundation platform for all agentic AI operations - a self-managing ecosystem that continuously monitors transport networks, analyzes patterns, and autonomously optimizes operations across multiple domains without human intervention.

Core Capabilities:

  • Autonomous network monitoring and analysis
  • Real-time pattern recognition across transport systems
  • Self-managing optimization without human oversight
  • Multi-agent coordination for complex infrastructure
  • Continuous learning from operational data
  • Autonomous incident detection and response
Agentic AI Multi-Agent Systems Autonomous Learning Google Cloud AI Real-time Processing

Results:

  • Fully autonomous operations reducing human intervention by 90%
  • Continuous self-optimization across transport networks
  • Emergent intelligence improving system performance over time
Agentic AI Autonomous

Autonomous Route Optimization Agent

Self-managing AI agent that continuously monitors EV fleet routes, analyzes real-time traffic patterns, and autonomously optimizes charging stop decisions without human intervention.

Agent Capabilities:

  • Real-time route recalculation with traffic integration
  • Autonomous decision-making for charging optimization
  • Predictive battery management with ML forecasting
  • Self-learning from historical patterns
  • Autonomous incident response and rerouting
LLM Agents Reinforcement Learning Real-time AI Google Cloud AI

Results:

  • Autonomous operation reducing manual intervention by 85%
  • Route efficiency improved through self-optimization
  • Cost reduction from autonomous decision-making
Agentic AI Autonomous

CAM Infrastructure Assessment Agent

Autonomous agent that continuously evaluates transport infrastructure readiness for autonomous vehicles. Self-manages data collection, analysis, and generates autonomous recommendations.

Agent Capabilities:

  • Autonomous infrastructure data collection from multiple sources
  • Real-time readiness scoring and gap analysis
  • Autonomous recommendation generation for improvements
  • Continuous monitoring of infrastructure changes
  • Autonomous priority ranking of improvements needed
Multi-Agent Systems Data Agents Analysis Agents NVIDIA Compute

Results:

  • Autonomous assessment of 50+ infrastructure dimensions
  • Real-time readiness tracking without manual audits
  • Accelerated infrastructure upgrade planning
Agentic AI Multi-Agent

Distributed Traffic Optimization Agent Network

Network of autonomous AI agents coordinating traffic flow across regions. Each agent makes autonomous decisions while communicating with neighboring agents for system-wide optimization.

Agent Capabilities:

  • Autonomous traffic flow optimization at local level
  • Peer-to-peer agent communication for regional coordination
  • Autonomous congestion prediction and prevention
  • Self-healing network with autonomous failure management
  • Emergent collective behavior without central control
Multi-Agent Orchestration Swarm Intelligence Distributed AI AWS/Google Cloud

Results:

  • Autonomous network operating 24/7 without human oversight
  • Traffic flow efficiency increased through distributed optimization
  • Emissions reduction from improved traffic management

Supporting AI-Powered Projects

Complementary solutions enabling our agentic AI ecosystem

Digital Twin

Digital Twin Platform

AI-powered virtual representation of physical transport systems, enabling agents to simulate and validate autonomous decisions before real-world deployment.

Features:

  • Real-time synchronization with physical infrastructure
  • AI simulation environment for agent testing
  • Scenario generation and optimization
  • Predictive analytics for system planning
NVIDIA Simulation Cloud Computing 3D Visualization
EV Optimization

EV Route Optimization

AI-driven optimization of electric vehicle routes considering battery capacity, charging infrastructure, and real-time traffic patterns.

Features:

  • Battery capacity-aware routing
  • Charging network integration
  • Real-time traffic adaptation
  • Fleet management optimization
Graph Algorithms ML Optimization Real-time APIs
Analytics

Transport Analytics & Insights

Advanced data analysis providing actionable insights for transport planning and optimization through machine learning and statistical modeling.

Features:

  • Traffic pattern analysis and forecasting
  • Infrastructure performance monitoring
  • Emissions tracking and reduction opportunities
  • Trend analysis and predictive modeling
BigQuery TensorFlow Data Science

MLOps Capabilities

How we take models from prototype to production: the live MLOps reference architecture behind our CAM Infrastructure Assessment Platform, following the Google Cloud MLOps Continuous Delivery pattern. Click any component below for full technical detail.

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MLOps Reference Architecture

Full interactive diagram of our CAM Infrastructure Assessment Platform pipeline — from feature store and model training through to on-device deployment and prediction service.

Open full detailed view →
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Commodities for Mobility

Live R Shiny dashboard exploring commodity supply chains and resource flows underpinning the transition to electric and autonomous mobility across the UK.

Launch live app →
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EV Mobility Analytics

Interactive analytics platform modelling electric vehicle adoption, charging infrastructure demand, and route efficiency across UK road networks.

Launch live app →

Ready to Transform Your Transport Network?

Contact us to discuss how our agentic AI solutions can optimize your infrastructure and operations.

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