Signs you need this
- Off-the-shelf models don't understand your sensor, signal or sequence data.
- You need predictions that respect known physics, not just patterns.
- Inference must run on-device where the cloud is too slow or unavailable.
Overview
Some problems need more than a pre-trained API: financial time series, sensor streams, industrial signals and control systems where the structure of the data matters and a general model is not enough.
We build and train custom deep-learning models in PyTorch and TensorFlow, including hybrid physics-ML architectures, and integrate them with perception and control pipelines for robotic and IoT systems.
How this differs from Data Science Solutions: Data Science Solutions covers tabular and classical ML; Deep Learning takes over when sequences, images, signals or physics demand neural architectures.
What we deliver
- Custom model developmentCNNs, RNN/LSTM, Transformers and PINNs designed for your data and constraints.
- Time-series predictionSequence models for forecasting, anomaly detection and regime identification.
- Perception pipelinesObject detection, tracking and segmentation feeding downstream decisions.
- Hybrid physics-MLModels that respect known physical laws while learning from data.
- Edge deploymentQuantised and optimised models for on-device or on-premise inference.
- Training infrastructureReproducible experiments, hyperparameter search and model registries.
Where it fits
Financial time-seriesSequence models for price, volatility and demand prediction with rigorous backtesting.
Industrial monitoringAnomaly detection on sensor data for predictive maintenance.
Robotic perceptionVision pipelines that give robots and drones situational awareness.
Scientific MLPhysics-informed models for simulation acceleration and inverse problems.
Technology we use
Chosen per project. We are vendor-neutral and will recommend what fits your constraints.
PyTorchTensorFlow / KerasTransformersPINNsOpenCVYOLOONNXOptunaWeights & BiasesCUDA
How we work
Problem framing
Define the target, the data available and what a good model must achieve.
Baseline & data pipeline
Establish a strong classical baseline and the pipeline the deep model will share.
Model development
Iterate architectures and training regimes with tracked experiments.
Deployment & monitoring
Serve the model, watch for drift and retrain on a schedule.
Typical first engagement
A problem-framing sprint with a strong classical baseline and a data pipeline, so the value of a custom deep model is proven before it is built.
Common questions
Do we need a lot of data?
Less than you might think. Transfer learning, augmentation and physics-informed constraints often make small datasets workable.
Can models run on-device?
Yes. We optimise and quantise models for edge hardware where latency or connectivity rules out the cloud.
How do you validate a deep-learning model?
Held-out sets that mirror production, walk-forward validation for time series, and shadow deployment before cut-over.
