Models are easy. Systems are hard.

Training, deployment, monitoring and drift detection as one continuous system rather than a model handed over a wall. It watches for silent degradation and says so early.

• Training, deployment, monitoring and drift as one system
• Silent degradation detected and announced early
• The difference between a model in production and one that used to work

A model that routes customer emails has been accurate for a year. In March a new product launches, the emails start using words the model has never seen, and its accuracy falls quietly: nothing throws an error. PROVING GROUND watches the inputs as well as the outputs, sees them drift away from the training data within days, and flags it with examples. The team retrains before customers notice.

Composition of models into systems that plan, call tools and act, with the routing, fallback and supervision that makes the whole more reliable than its parts. Each step is attributable.

• Models composed into systems that plan, call tools and act
• Routing, fallback and supervision around every step
• Each step attributable when the output is wrong

A claims pipeline chains four models: read the document, extract the fields, check for fraud signals, draft the decision. When a decision comes out wrong, every step’s input and output are on the record, so the fault is traced to the extraction step, which read a day-first date as month-first. Documents the models are unsure of go to a person instead of being guessed at.

High-fidelity spatial and sensor data captured, structured and labelled at training scale, with the capture conditions recorded alongside the measurements.

• Spatial and sensor data captured and labelled at training scale
• Capture conditions recorded alongside the measurements
• Provenance kept, because it cannot be reconstructed later

Every scan in a body-scan corpus is stored with how it was captured: the device, the light, the distance and the consent. Two years later a model behaves differently on scans from one device; the corpus is split by device in one query, and the cause is found. Without metadata recorded at capture time, the question could not have been answered at any price.