Industrial AI & Edge Computing
Inference · Vision · Analytics · Optimisation
AI That Runs Where It Matters — On the Device
We bring AI and data processing closer to where it matters — at the device level. Our team integrates machine learning models with embedded hardware, enabling local inference, real-time decision-making, and reduced reliance on cloud connectivity.
From neural network optimisation for edge devices to full computer vision pipelines for industrial inspection, we deliver AI that works in production — fast, reliable and secure. No cloud dependency. No latency. Just results, on the device, in real time.
End-to-End Edge AI Engineering
From model development and optimisation through to on-device deployment and monitoring — we cover the full edge AI pipeline.
Edge AI Deployment
Deploying optimised ML models on edge hardware (NVIDIA Jetson, NXP i.MX, STM32, Coral) for real-time on-device inference. We handle the full stack from model to silicon.
Computer Vision
Object detection, classification, segmentation and tracking pipelines for industrial inspection, safety monitoring and quality control — running in real time at the edge.
Predictive Maintenance
Sensor-driven ML models that detect anomalies, predict equipment failures and trigger maintenance alerts before downtime occurs. Data-driven reliability.
Model Optimisation
Model pruning, quantisation, knowledge distillation and architecture search to run complex neural networks on resource-constrained embedded hardware — without sacrificing accuracy.
Data Pipeline Engineering
Edge-to-cloud data pipelines for collecting, processing and acting on sensor, video and time-series data in real time. Efficient, reliable, production-grade.
MLOps for Embedded
Model versioning, performance monitoring, A/B testing, and over-the-air model updates for deployed edge AI systems. Keep your models sharp in the field.
From Use Case to Deployed AI in 4 Steps
Use Case Assessment
We evaluate your problem, data and hardware constraints. We define what AI can realistically achieve and recommend the right approach — no hype, just engineering.
Model Development
Data pipeline, model training, and optimisation for your target hardware. We iterate until performance meets your accuracy and latency requirements.
Edge Integration
Model deployment on your embedded platform, system integration, and real-world validation. We test in your environment, not just on benchmarks.
Deployment & Monitoring
Production rollout, OTA model updates, drift detection, and ongoing performance monitoring. Your AI stays accurate as conditions change.
AI Engineers Who Understand Hardware
- ✅ Hardware + AI together — we optimise models for real embedded platforms, not just cloud GPUs
- ✅ Production-grade — our AI systems run 24/7 in industrial environments — not just in demos
- ✅ Real-time focus — low-latency inference on constrained hardware, designed for production throughput
- ✅ End-to-end ownership — from data pipeline to deployed model to ongoing monitoring
- ✅ Domain experience — industrial inspection, predictive maintenance, video analytics, smart infrastructure