AI & Machine Learning
Strategy and hands-on development of supervised, unsupervised, and predictive models — turning your data into measurable, real-world outcomes.
scikit-learnClassificationRegression
Capabilities
Artificial Intelligence & Machine Learning
We've extended A&W's deep systems engineering heritage into modern AI — with an emphasis on edge AI and physical AI: models that run on-device, next to the sensor, and perceive and act in the physical world. From sensor data and signal chains to deployed models, we cover the full pipeline.
Edge AIPhysical AI
PythonPyTorchOpenCV
PyVisionNumPyPandas
scikit-learnCUDA / GPUMLOps
End to end
Our AI & ML expertise
A complete machine learning capability — from raw data and feature engineering through model development, optimization, automation, and production integration — with particular depth in edge AI and physical AI.
Deep Learning
Neural network architectures — CNNs, RNNs/LSTMs, transformers, and custom networks — for image, signal, and sequence problems.
CNNTransformersTransfer Learning
PyTorch
Production-grade model development in PyTorch — from research prototyping to optimized, exportable models ready for deployment.
PyTorchTorchScriptONNX
Computer Vision
Image and video understanding — detection, segmentation, classification, and measurement — built with OpenCV and PyVision.
OpenCVPyVisionDetectionSegmentation
Training, Evaluation & Optimization
Robust training pipelines, rigorous evaluation metrics, hyperparameter tuning, quantization, and pruning for accurate, efficient models.
Hyperparameter TuningQuantizationMetrics
Data Preprocessing & Feature Engineering
Cleaning, labeling, augmentation, and feature design that turn raw, messy data into high-quality training sets.
PandasNumPyAugmentation
Automation using AI Pipelines
Automated, reproducible pipelines for data ingestion, training, validation, and retraining — keeping models fresh and reliable.
MLOpsCI/CDOrchestration
AI Integration into Production Systems
Deploying models into real products — edge and embedded first, plus on-prem and cloud — with monitoring, versioning, and the reliability our customers expect.
Edge / EmbeddedAPIsMonitoring
Where we go deepest
Edge AI & Physical AI
Our AI work centers on models that leave the data center — running on-device at the edge, and sensing, deciding, and acting in the physical world.
Edge AI
Inference on the device itself — embedded processors, GPUs, and FPGAs — where latency, power, privacy, or connectivity rule out the cloud. We quantize, prune, and accelerate models to fit real hardware budgets, drawing on the same embedded and FPGA teams that design the boards.
- On-device inference on embedded CPU, GPU & FPGA targets
- Quantization, pruning & acceleration to hit power and latency budgets
- Model export & runtimes — TorchScript, ONNX
- Monitoring, versioning & field updates for deployed devices
Edge AIOn-device InferenceQuantizationONNXFPGA
Physical AI
AI that perceives and acts in the physical world — instruments, machines, and automation that close the loop from sensor to decision to action. Our signal-chain, sensor, and control heritage means the model is designed with the physics, not bolted on afterward.
- Sensor fusion & perception built on real signal chains
- Real-time inference inside control & measurement loops
- AI-assisted instruments — ultrasound imaging & industrial systems
- From benchtop prototype to field-deployable system
Physical AISensor FusionReal-timeIndustrial Automation
How we work
From data to deployed model
A disciplined, system-oriented process — the same rigor we bring to medical devices, applied to AI.
Data & problem framing
Define success metrics, gather and engineer features from your data and sensor streams.
Model development
Prototype, train, and evaluate models in PyTorch — iterating quickly toward target accuracy.
Optimization
Tune, quantize, and accelerate for the target hardware — GPU, edge, or embedded.
Integration & MLOps
Deploy into production with automated pipelines, monitoring, and retraining.
Related capabilities
Related engineering capabilities
Machine learning rarely arrives on its own. These are the disciplines it usually has to fit alongside — each with its own page.
Custom AI Models & Training
Custom model development, training and evaluation, quantisation for edge targets, production integration.
Read more →Embedded Software
Getting a model inside a device's power and latency budget, alongside the firmware it runs next to.
Read more →Signal Processing & DSP
Feature and sensor-data engineering ahead of a model, and the classical processing it often replaces or complements.
Read more →FPGA, ASIC & RTL Design
Where inference or pre-processing has to happen in fabric to keep up with the sensor.
Read more →Bring AI into your product
Whether it's edge AI on your device, physical AI in your instrument or machine, computer vision, or production ML, we'll help you build it and ship it.