Transformers and attention
Encoder, decoder and encoder-decoder models for language, code, logs and any long sequence where context matters.
Research
Neural networks, built to earn their place.
Our research track is where deep learning gets pressure tested before it reaches your product. We reproduce the paper, run it on your data, and keep only what beats a simple baseline.
01
Baseline first, always
02
Every result reproduced before we trust it
03
Measured on the outcome, not the benchmark
The toolkit
Architectures we work with.
Eight families of neural network we use in production, each with the engineering around it to stay reliable.
Convolutional vision models
Detection, segmentation and classification backbones, with modern ConvNet and vision transformer hybrids where they earn it.
Sequence and time series
Temporal models for forecasting, anomaly detection and event prediction, from classical recurrent nets to temporal transformers.
Representation learning
Contrastive and self-supervised training that turns raw data into embeddings search, matching and retrieval can use.
Generative models
Diffusion for images and audio, language models for text, with the evaluation and guardrails to run them in front of users.
Graph neural networks
Message-passing models for data that is a network: fraud rings, supply chains, molecules, recommendations.
Reinforcement and decision models
Policy learning and contextual bandits for sequential decisions where a fixed rule set stops keeping up.
Efficient inference
Quantisation, distillation and pruning to fit a model inside a real latency and cost budget, on a GPU or at the edge.
How the model stays trustworthy
Training and serving, done properly.
Training
- Reproducible runs with tracked data, code and model versions
- Honest baselines and ablations before any claim of a gain
- Held-out and time-split validation that matches how the model will be used
- Data and label quality checks built into the pipeline
Serving
- Batched and streaming inference sized to a target response time
- Quantised or distilled to the smallest model that still passes
- Drift, accuracy and latency monitored with alerts
- Automatic rollback when a new model regresses on held-out tests
From paper to production
How a technique earns its place.
- 1
Read and reproduce
Rebuild the published result on a public dataset. A paper is a claim until it runs.
- 2
Prototype on real data
The technique meets your data and your constraints, not a clean benchmark. Most ideas fall over here.
- 3
Measure honestly
Against a simple baseline, on a metric that maps to a real outcome. A small gain at ten times the compute is not a gain.
- 4
Ship or shelve
If it wins, it goes into the production pipeline with monitoring. If not, we write up what we learned.
We also spend lab time on harder frontiers, including brain-computer interfaces: the same deep learning toolkit applied to noisy EEG and neural time series. This is exploratory research, not a service. There is no shipped product behind it, and we do not build medical devices.
Work with the research track
Start a conversation Bring a hard problem.
A funded proof of concept, a second opinion on whether a technique is ready, or a longer research partnership. We are open to all three.
Have a problem that does not fit a template?
Tell us what you’re working on. We’ll work out the next step together.
Let’s talk about your project