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

Training, deployment, monitoring and drift detection as one continuous system rather than a model handed over a wall. A model degrades quietly — the inputs move, the world moves, and nothing throws an error. PROVING GROUND watches for that and says so early, which is the entire difference between a model in production and a model that used to work.

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

Composing models into systems that plan, call tools and act — with the routing, fallback and supervision that makes a composition more reliable than its parts rather than less. Each step is attributable, so when the output is wrong it is possible to find out which link produced it.

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

High-fidelity spatial and sensor data captured, structured and labelled at training scale — with the capture conditions recorded alongside the measurements. A corpus without provenance is a corpus you cannot trust twice, and the metadata cannot be recovered after the fact.