We design, build, and deploy machine learning systems for complex operations, from the factory floor to clinical review. You get a practical solution that fits your data, your decisions, and your infrastructure.
One engineering partner. Multiple operational frontiers.
We start with the operational decision, not the model. Each solution is shaped around your machines, constraints, teams, and success criteria.
Recommend better process settings from material properties, machine data, quality results, and historical production performance.
Optimise schedules around electricity prices, machine consumption, CO₂ intensity, deadlines, and capacity.
Use machine and sensor data to predict failures before they happen and recommend the right maintenance action.
Detect scratches, dents, coating issues, edge damage, stains, and cracks on coils and sheets in real time.
Our reusable engineering foundation meets domain-specific workflows, data, safeguards, and deployment requirements.
Process optimisation, quality vision, reliability, and planning for production environments.
View applications ↗Forecasting, flexible demand, asset intelligence, and decision support under uncertainty.
Discuss your use case ↗Private document intelligence, longitudinal patient summaries, and traceable clinical review.
See medical application ↗Custom decision systems for data-rich processes that off-the-shelf software cannot solve.
Start a conversation ↗Many teams need better reporting, data products, and operational tools before or alongside machine learning. We build those too.
Clear operational reporting for production, energy, quality, maintenance, and management teams. We connect the data, define the measures, and design reports people can actually use.
Focused internal tools for forecasting, optimisation, model review, and data exploration. They are ideal when a spreadsheet is no longer enough but a large software project would be overkill.
Reliable data models, pipelines, and governed analytics foundations in Snowflake. We prepare operational data so dashboards and machine learning systems share one dependable source.
We combine ML engineering, product thinking, and close work with domain experts. The result is a clear path into production, not another slide deck.
Align the use case, available data, operating constraints, and business metric.
Build a focused prototype against real examples and an agreed baseline.
Integrate models, interfaces, monitoring, and human review into the workflow.
Run privately or on premises, measure impact, and evolve with new data.
Bring us the process, bottleneck, or decision. We’ll help you find the shortest credible path from data to measurable impact.