All services
02 — Service Domain
Edge AI & TinyML Solutions
Intelligence on a chip
Real-time intelligence on the device — we train, quantise and deploy ML models that run on microcontrollers with kilobytes of RAM.
TinyML stack
- TensorFlow Lite Micro
- Edge Impulse
- CMSIS-NN
- Python
- C++
Sectors
- / Predictive Maintenance
- / Healthcare Wearables
- / Smart Agriculture
- / Industrial Safety
§ TinyML Performance Optimizer
Quantisation Sandbox
See how converting model weights from float32 to int8 reduces footprint and latency on MCUs while maintaining accuracy.
1. Choose Model Type
2. Toggle Precision
Target:Cortex-M4 @ 80MHz
Flash required:85 KB
Execution time:22 ms
Inference accuracy:94.5%
METRICS COMPARISON: FP32 VS INT8TFLITE COMPATIBLE
§ Capabilities
What's included
- TinyML Development
- TensorFlow Lite for Microcontrollers
- Edge AI Deployment
- Predictive Analytics
- Anomaly Detection
- Smart Sensor Intelligence
§ Outcomes
What you walk away with
- 01
Sub-100ms inference on MCU-class hardware
- 02
Models quantised to int8 with minimal accuracy loss
- 03
Edge-resident anomaly and classification pipelines
- 04
Privacy-preserving on-device intelligence
§ Process
How we work
- 01
Data collection from device
Sensor sampling, labelling and dataset versioning.
- 02
Model design & training
Compact architectures tuned for the target MCU.
- 03
Quantisation & conversion
TFLite Micro / CMSIS-NN pipelines and benchmarking.
- 04
Field deployment
On-device evaluation, drift monitoring, model OTA.
§ FAQ