Our Technology

Cutting-Edge IoT, AI & Edge Computing at Your Fingertips

Built on a foundation of advanced technology, our platform delivers real-time intelligence, unmatched reliability, and seamless scalability.

Edge AI Processing

Intelligent offline processing for real-time alerts with minimal latency and reduced data costs. Our edge computing nodes process data locally for instant decision-making.

Global IoT Network

Utilizes LoRaWAN, NB-IoT, and 5G for reliable network coverage across urban and rural areas. Seamless connectivity wherever your fleet operates.

Real-time Data Pipeline

Sub-second data ingestion and processing pipeline capable of handling millions of events per second with guaranteed delivery and ordering.

Cloud-Native Architecture

Built on Kubernetes with auto-scaling capabilities. Multi-region deployment ensures 99.99% uptime and low-latency access worldwide.

Enterprise Security

End-to-end encryption, SOC 2 Type II compliance, and role-based access control. Your data is protected with bank-grade security measures.

Cross-Platform SDKs

Native SDKs for iOS, Android, and web platforms. Easy integration with existing systems through RESTful APIs and webhooks.

Platform Architecture

Built for Scale & Reliability

Our multi-layered architecture ensures seamless data flow from device to dashboard, with built-in redundancy and fault tolerance at every level.

Device Layer

  • GPS Trackers
  • OBD-II Devices
  • IoT Sensors
  • Mobile Apps

Processing Layer

  • Edge Computing
  • Stream Processing
  • AI/ML Models
  • Event Processing

Data Layer

  • Time-Series DB
  • Graph Database
  • Data Lake
  • Real-time Cache

Application Layer

  • Web Dashboard
  • Mobile Apps
  • APIs
  • Integrations
AI Technology

The Intelligence Inside TransitFlow

AI is not a feature — it is the core of the platform. Every route, alert, and prediction is generated by machine learning models trained on African logistics data.

Data Sources

  • Real-time GPS telemetry (10-second intervals)
  • OBD-II vehicle diagnostics & engine sensors
  • Historical route and fuel consumption data
  • Live traffic feeds: Lagos, Abuja, Port Harcourt
  • Driver behaviour logs (speed, braking, idling)
  • Weather and road condition APIs

AI Capabilities

  • Optimal multi-stop route generation (RL models)
  • Predictive vehicle maintenance (LSTM networks)
  • Driver anomaly & fatigue detection (CV)
  • Fuel theft and idling waste detection
  • Demand surge forecasting for fleet staging
  • Real-time ETA prediction with traffic awareness

Models & Techniques

  • Reinforcement learning for adaptive routing
  • LSTM neural networks for predictive maintenance
  • Computer vision for dashcam-based monitoring
  • Graph Neural Networks for traffic modelling
  • Time-series forecasting for demand prediction
  • Anomaly detection for fraud prevention

Why GPU Compute Is Required

  • Real-time inference across thousands of vehicles
  • Training CV models on dashcam video footage
  • Parallel route optimisation for large fleets
  • Processing millions of sensor events per second
  • On-device edge AI in vehicle-mounted units
  • Accelerated retraining as new trip data arrives
Cloud & GPU Infrastructure

Powered by AWS & NVIDIA Technologies

Our infrastructure plan uses AWS for cloud backbone and NVIDIA for GPU-accelerated AI — enabling us to scale from a handful of beta vehicles to tens of thousands across West Africa without rebuilding.

Amazon Web Services (AWS)

Cloud backbone for the entire platform

Amazon S3: Telemetry data storage, dashcam recordings, and backups at scale
Amazon RDS: Managed database for fleet profiles, routes, and delivery records
AWS Lambda / ECS: Serverless APIs and containerised microservices for real-time processing
Amazon CloudFront: Fast content delivery for dashboards across Nigeria and West Africa
Amazon Bedrock: AI dispatching assistant and natural-language fleet query interface
AWS IoT Core: Secure connectivity for thousands of in-vehicle GPS and sensor devices
Amazon CloudWatch: Real-time monitoring, performance alerts, and platform observability

NVIDIA Technologies

GPU acceleration for AI workloads

CUDA: GPU-parallel route optimisation and machine learning model training
TensorRT: Optimising AI inference latency for real-time anomaly detection and driver scoring
NVIDIA Jetson: Edge AI modules deployed inside vehicles for on-device computer vision and offline alerting
Triton Inference Server: Serving multiple AI models at scale with consistent SLA and throughput
RAPIDS: GPU-accelerated data science for fleet analytics and batch processing pipelines
DeepStream: Video analytics pipeline for processing dashcam streams and driver behaviour detection
99.99%
Uptime SLA (target)
<100ms
API Latency (target)
10M+
Events/Second capacity
50+
Integrations planned

Want to Learn More About Our Technology?

Schedule a technical deep-dive with our engineering team.