Clear, owned outputs.
- Approach and data requirements
- Trained and fine-tuned models
- Evaluation results
- Optimized deployment artefacts
- Serving API or edge package
Deep learning uses neural networks to learn from complex, unstructured data such as images, text, audio and sensor signals. It powers capabilities that traditional methods struggle with. We apply deep learning where it is genuinely the right tool, often starting from pre-trained models to reduce data and compute needs.
The problems Deep Learning solves.
Neural networks for images, text, audio and time series, trained and deployed efficiently.
Traditional models can't handle images, audio or free text well.
Training from scratch seems to require impossible amounts of data.
GPU costs make experimentation expensive.
Models are too large or slow to run where they're needed.
What Deep Learning covers.
Transfer learning
Adapting pre-trained models to your task and data.
Fine-tuning
Specializing models for your domain with modest labelled data.
Architecture selection
CNNs, transformers and sequence models chosen for the task.
Training infrastructure
Efficient GPU training on cloud platforms.
Model optimization
Quantization, distillation and pruning for faster inference.
Edge deployment
Running models on devices where latency or connectivity matter.
When deep learning is the right tool.
Deep learning shines on images, audio, text and other unstructured data, and when there's plenty of it. On small tabular datasets simpler models usually win.
- Transfer learning
- Starting from a pre-trained model and fine-tuning it on your data, which cuts the data and compute needed.
- Architectures
- Convolutional networks and vision transformers for images, transformers for text and sequences.
- Data labelling
- A labelling guide, quality checks on labels and active learning to label the most useful examples first.
- Training infrastructure
- GPU instances sized to the job, experiment tracking and reproducible training runs.
- Deployment
- Model compression, batching and the right serving hardware to keep latency and cost acceptable.
Why it's worth it.
New capabilities
Work with data that was previously unusable.
Less data needed
Pre-trained models reduce labelling effort.
Efficient inference
Optimized models run faster and cheaper.
When Deep Learning is the right move.
- Visual inspection for manufacturing quality.
- Classifying documents or messages by meaning.
- Forecasting from sensor or time-series data.
Technologies & platforms
- PyTorch
- TensorFlow
- Hugging Face Transformers
- ONNX
- TensorRT
- NVIDIA GPUs
- Vertex AI
- SageMaker
How Deep Learning work runs.
- 01
Frame
The decision or task to improve, the measure of success and the constraints.
- 02
Assess data
Availability, quality, access, privacy and labelling needs.
- 03
Prototype
A small, honest test of whether the approach can work.
- 04
Build and evaluate
Model development with documented evaluation against agreed criteria.
- 05
Deploy and monitor
Integration, monitoring for drift and performance, and retraining plans.
Deep Learning questions.
Is deep learning always better?
No. For many tabular problems, simpler models perform as well or better and are easier to explain. Deep learning shines on images, text, audio and complex signals.
How much labelled data is needed?
With transfer learning, often far less than people expect. We estimate needs during a short feasibility phase.
Can models run without the cloud?
Yes. Optimized models can run on edge devices or on-premises servers when connectivity, latency or privacy require it.
Talk to us about deep learning.
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.