Edge AI / TinyML
Running intelligence directly on microcontrollers and edge hardware
Cloud-free inference — millisecond latency, zero bandwidth, on a coin-cell budget.
What This Service
Actually Covers
TinyML brings machine learning inference to microcontrollers with kilobytes of RAM. We design, train, quantise, and deploy ML models to MCUs and edge devices using TensorFlow Lite Micro, Edge Impulse, and ONNX Runtime — enabling on-device anomaly detection, keyword spotting, gesture recognition, and visual inspection without cloud dependency.
Expert Engineers
Projects Delivered
Client Satisfaction
Support Available
Everything Included
A detailed breakdown of every capability we bring to this engagement — no hidden scope.
Model architecture selection optimised for MCU memory constraints
Post-training quantisation (INT8, FP16) and quantisation-aware training
Model pruning and knowledge distillation for size reduction
TensorFlow Lite Micro deployment on ARM Cortex-M
Edge Impulse pipeline: data collection, training, deployment
ONNX Runtime deployment on Raspberry Pi, Jetson Nano, and similar
Keyword spotting, wake-word detection, and audio classification
Gesture recognition and motion classification on IMU data
How We Approach
This Service
A clear, structured methodology tailored specifically to this engagement.
Constraint Mapping & Dataset Design
Define MCU RAM/Flash budget, latency target, and design the data collection protocol for the target task.
Model Design & Training
Design a model within the compute budget, train with appropriate augmentation, and validate accuracy.
Quantisation & Optimisation
Quantise the model, evaluate accuracy degradation, apply optimisation until latency and size targets are met.
MCU Deployment & Integration
Deploy to target MCU, profile on-device latency and RAM usage, and integrate with the firmware application.
Ideal For
IoT products requiring on-device intelligence — sensors detecting anomalies, devices recognising gestures, or audio devices doing keyword spotting offline.
What You Receive
Quantised model (TFLite/ONNX), MCU-ready inference code, deployment guide, accuracy/latency benchmark report.
Often Combined With
These services frequently complement Edge AI / TinyML in the same project.
Predictive Analytics
Anticipating failures, demand, and anomalies before they happen
Explore →Computer Vision Pipelines
Teaching machines to see — object detection, classification, and visual inspection
Explore →Custom ML Model Development
End-to-end machine learning from problem definition to deployed model
Explore →More Services in
This Category
Explore the other specialised services we offer under AI & Machine Learning.
Predictive Analytics
Anticipating failures, demand, and anomalies before they happen
Computer Vision Pipelines
Teaching machines to see — object detection, classification, and visual inspection
NLP & LLM Integrations
Intelligent text understanding, generation, and document processing
Custom ML Model Development
End-to-end machine learning from problem definition to deployed model
Model Monitoring & Retraining
Keeping your deployed models accurate as the world changes around them
Ready to Start Your
Edge AI / TinyML Project?
Tell us about your project and we'll put together an honest, detailed proposal — no lengthy sales pitch.