AI/ML
Custom neural network architectures designed, trained, and optimised for your specific prediction or pattern-recognition problem.
0h
Response time
0+
Projects delivered
0+
Years in production
What it is
Deep learning models are multi-layer neural networks trained to learn hierarchical representations from raw data — images, audio, time series, or text — enabling pattern recognition and prediction on tasks that cannot be solved with hand-engineered features.
What you get
Not every ML problem needs deep learning, and not every deep learning problem needs a transformer. CNNs for spatial data, RNNs and transformers for sequences, graph neural networks for relational data — we select architectures based on data structure, training budget, and inference requirements, not on what is currently popular.
We handle the complete model lifecycle: dataset curation and preprocessing, architecture design, GPU-accelerated training on cloud infrastructure, hyperparameter optimisation, quantisation and pruning for deployment, and monitoring in production. Everything is reproducible — versioned datasets, tracked experiments, documented training runs.
Production readiness is built in from the start. A model that achieves 97% accuracy on a test set but runs at 500ms on the inference server is not a production model. We set latency and throughput targets during scoping and validate against them before handover.
Key capabilities
Each engagement is scoped to your requirements — these are the core capabilities we bring to the table.
Transfer learning and domain adaptation
Model compression: quantisation, pruning, distillation
GPU-accelerated training on cloud infrastructure
Experiment tracking with MLflow and Weights & Biases
Reproducible training pipelines with DVC
Our process
A structured, engineering-led approach that moves from understanding your goals to a production system — with no handoff surprises.
Typical engagement
8–16 WEEKS
We map your goals, constraints, and existing infrastructure. Scope is defined and success criteria agreed before any development begins.
We design the technical approach, select the right tools, and produce a milestone-driven delivery plan with no ambiguity.
Iterative development with regular demos. Code reviews, test coverage, and documentation happen in parallel — not at the end.
Production release with monitoring setup and handover documentation. We stay close during the first weeks post-launch.
Built with
Deep learning is warranted when the signal in your data is high-dimensional and hierarchical — images, audio, raw sensor streams, unstructured text — and when you have enough data to justify training. For tabular data with fewer than 100K rows, gradient boosted trees typically outperform deep learning and are faster to develop.
Simple transfer-learning fine-tunes can complete in hours. Training a large model from scratch on a custom dataset can take days to weeks on GPU clusters. We optimise for training cost by using the smallest viable architecture and pre-trained initialisation wherever possible.
You do. All model artefacts, training code, and data pipelines are delivered to you and are your intellectual property. We retain no rights to models trained on your data.
Work with us
Share what you're building — we'll respond within one business day with questions or a proposal outline.
