Computer vision
Image-based quality inspection, surface defect detection, code and meter reading, counting and conformity checking from your own captures.
Capabilities
Applied artificial intelligence and R&D
A costed proof of concept before any commitment. If the gain isn't measurable, we say so.
The symptom you'll recognise
AI has been pitched to you as a general answer, with no number attached to your installation. Or you have a specific, recurring problem — a quality check human eyes can't hold for eight hours, a critical asset that fails without warning — and you don't know whether it's tractable.
Artificial intelligence produces industrial value on a narrow set of well-posed problems: catching a defect a tired operator misses, anticipating a failure before the line stops, forecasting demand to size a buffer. We work on those, and we turn the rest down.
No commitment — you leave with an order of magnitude.
What we do
Predictive maintenance
Anomaly detection models over your operating and failure history, so an intervention is triggered before unplanned downtime rather than after.
Forecasting and optimisation
Demand forecasting, production and route plan optimisation, safety stock sizing — built from your actual history.
Bounded proof of concept
Every topic starts with a short phase at fixed scope and budget, with a success criterion agreed in advance. If the criterion isn't met, we don't recommend industrialisation.
What you receive
Deliverables, not promises
- Feasibility study with a quantified success criterion
- Proof of concept evaluated on your real data
- Industrialised model, monitored and documented
- Retraining procedure and drift monitoring
- Knowledge transfer to your teams
Frequently asked questions
We have no data history. Is that a blocker?
Often yes, for predictive maintenance, which needs a failure history. In that case we start by putting collection in place: it's a prerequisite, and saying so immediately saves you funding a model that could never learn.
How long before we know whether a use case is viable?
Feasibility is usually a matter of weeks. It ends either in a measured result on your data or in an argued negative — which saves you considerably more.
Does our data have to leave the company?
Not necessarily. Depending on the workload, training and inference can stay on your infrastructure. We settle this before any data moves.
Let's talk about your artificial intelligence and r&d project
A conversation with someone who has handled this class of problem before. You leave with a clear read, whether or not we work together.
No commitment — you leave with an order of magnitude.