Service
AI & Machine Learning
Models that run where the data already is — on the device or beside your own service.
We add machine learning to products where it changes what the product can do, not where it decorates a feature list. Two patterns cover most of the work we have shipped. The first is on-device inference: a model runs on the phone, so there is no round trip to a server, no video leaving the handset, and no per-request bill — this is how the pose and movement tracking in our fitness platform works. The second is a dedicated inference service sitting beside the main application, consuming a stream of readings and raising an alert when something looks wrong — this is how health-anomaly detection works in our pilgrim-safety platform, combining explicit thresholds with a learned model. In both cases the model is one component in a system that still has to be correct, observable, and maintainable.
What's included
- Assessing whether a problem actually needs a model
- On-device inference for mobile, keeping data on the handset
- Inference services that consume live streams and raise alerts
- Threshold rules alongside learned models, so behaviour stays explainable
- Integration into the existing application and its alerting path
- Monitoring what the model does once it is live
FAQ
Questions clients ask
Does our data leave our systems?
Not with on-device inference — the model runs on the phone and only results are sent anywhere. Where a server-side model is required, it runs in your own infrastructure alongside your application.
How do we know why the model made a decision?
We pair learned models with explicit threshold rules wherever the decision matters. The rules are readable and testable, so behaviour can be explained rather than guessed at.
What if machine learning is not the right answer?
Then we say so during the assessment, before anything is built. A rule you can read and test beats a model you cannot.
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