Neural architectures built for your data, not borrowed from someone else's.
Custom neural network design, transfer learning, model compression, and multi-modal architectures for problems that simpler models can't solve.
Deep learning — neural networks with many layers trained on large datasets — is the technology behind most state-of-the-art AI results in vision, language, and audio. The practical question isn't whether to use deep learning; it's which architecture fits your data modality, volume, and latency requirements, and whether you need a custom model or transfer learning from an existing foundation model. We answer that question accurately and build accordingly.
10–100×
performance gain over classical ML on unstructured data tasks
70%
of production deep learning models use transfer learning, not training from scratch
4–16 wks
typical custom architecture design-to-deployment timeline
What's included
Services within Deep Learning
Each is a scoped engagement. Tell us which one fits your situation — or book a call and we'll scope it together.
Neural Architecture Design
Custom CNN, RNN, Transformer, and hybrid architecture design for your specific data modality, task, and compute constraints — with ablation studies to justify architectural decisions.
Transfer Learning & Fine-Tuning
Fine-tuning foundation models (ViT, CLIP, BERT, Whisper, LLaMA) on domain-specific datasets — with layer freezing strategies, learning rate scheduling, and overfitting controls for small data regimes.
Model Compression
Pruning, quantisation (INT8/FP16), knowledge distillation, and neural architecture search to reduce model size 4–20× with minimal accuracy loss — for edge deployment and cost reduction.
Hyperparameter Optimisation
Automated hyperparameter search using Optuna, Ray Tune, and Bayesian optimisation — finding configurations that outperform manual tuning in a fraction of the time.
Multi-Task & Multi-Modal Learning
Shared-representation architectures that learn from multiple tasks or data types simultaneously — improving generalisation and reducing the need for task-specific training data.
Self-Supervised & Contrastive Learning
Pre-training on unlabelled domain data using self-supervised objectives (SimCLR, DINO, MAE) to build useful representations before fine-tuning on small labelled datasets.
The problem
Where deep learning projects get derailed
These aren't edge cases — they're what we hear on almost every discovery call. If any of them sound familiar, this is likely the right place to start.
Training from scratch is expensive and usually unnecessary — most teams don't evaluate transfer learning options before committing compute budget
Architecture selection is treated as a default rather than a design decision — the wrong backbone wastes months
Hyperparameter tuning is done manually when automated approaches (Optuna, Ray Tune) find better configurations faster
Model compression is an afterthought — models that worked in training are too large for inference targets and have to be rebuilt
Multi-modal requirements (text + image + tabular) are underspecified upfront, leading to integration failures late in the project
Who it's for
This is the right fit if…
These systems work best for organisations at a specific point — where the problem is real, the data exists, and generic tools have already proved insufficient.
Research teams pushing accuracy on a specific benchmark beyond what commercial APIs deliver
Product teams embedding AI into applications where latency and cost constraints rule out cloud APIs
Businesses with proprietary datasets large enough to justify custom training
Engineering teams inheriting deep learning code they need to optimise or extend
Common questions
What people ask before they book
Not sure where to start?
Talk it through on a free call.
We'll help you figure out which of these fits your situation — no pressure, no obligation.
Book a Free 30-Min Call