Specialised models · Trained on your data · Built for your domain
We build machine learning models trained on your data, shaped to your specific problems and goals — purpose-built for your domain from the ground up.
It's AI that empowers people and broadens the pool of specialists, not replaces them.
Our models are small and their tasks are specialised. Our cooperative pipelines can be broad and long.
What we do
From problem framing to production deployment, we build ML systems that work in the real world.
Statistical analysis, experimentation, and exploratory work that turns raw, messy data into clear, actionable insight.
Architecture reviews, data governance, and roadmap planning to build the right foundations before investing in models.
Performance compounds with each iteration
Illustrative — every engagement is different, but the pattern holds - system performance improvements gated on research, investigations, remodelling, probes and implementation.
In the field
Right now, on five continents, machines are running software we've worked on.
Some of the highest-throughput, harshest processing environments in the world. Dust, vibration, mixed and contaminated materials — models that hold up when conditions are at their worst.
Food-grade lines where a misclassification has direct commercial cost. Clean rooms, controlled lighting, high and variable quality standards — precision at scale.
Data science and machine learning, applied
Client engagements are confidential. These domains reflect the types of problems we work on.
Demand, sales, and event-rate forecasting built and tuned on historic operational data. We handle cold-start constraints, hierarchical reconciliation, and multi-step horizons — selecting between statistical baselines, gradient boosting on lag features, and sequence architectures based on data volume and latency needs.
Read more →Custom vision models trained on your images, not generic APIs applied to your problem. We scope annotation pipelines, curate training sets, and select architectures — YOLO-family detectors, segmentation models, or domain-specific variants — based on object scale, inference speed, and edge-vs-cloud requirements.
Read more →Most production ML problems are tabular. We build gradient boosting pipelines with rigorous attention to leakage, class imbalance, and probability calibration — and know when the right model is a logistic regression.
Read more →Identifying a pattern in data is not the same as finding a lever you can pull. We design experiments with correct power analysis, handle interference and network effects, and apply defensible observational methods when randomisation isn't feasible.
Read more →A model that performs well offline and degrades silently in production is a liability. We build the surrounding infrastructure — feature pipelines, serving layers, drift detection, and retraining triggers — that keeps models reliable after launch.
Read more →The quality of a model is bounded by the quality of its training data. We design collection strategies, build annotation pipelines, define labelling schemas, and implement quality control processes — including dataset versioning and maintenance as ground truth evolves over time.
Read more →Model outputs are only useful if people engage with them. We design interactive dashboards, visualisation layers, and gamification mechanics that make data-driven insights accessible and actionable — turning predictions into decisions people actually act on.
Read more →We apply reinforcement learning to sequential decision-making problems — pricing, inventory allocation, recommendation ordering, and control systems. We design reward structures and training environments that align learned policies with real business objectives, with close attention to safe exploration and deployment constraints.
Read more →Tell us about your project and we'll get back to you within one business day.