洞察

A product decision guide to exposure, intervention, recovery, memory, generalization, learning from experience and cost per successful outcome.

A vendor-independent decision guide for choosing the role, authority, evidence and lifecycle of world models, VLAs, robot policies, simulators and independent monitors.

Ninety days is a governance clock for reaching an evidence-backed proceed, change, partner, pause or stop decision—not a universal promise that every pilot can reach production.

Classify an AI product by the authority it receives, the environment it changes and the evidence it needs—not by the model family in its architecture diagram.

A source-backed pre-mortem for four system boundaries that model benchmarks miss: deployment artefacts, thermal and power limits, field inputs, and fleet updates and recovery.

A source-reviewed classification method for robotics, machinery, mobility, critical infrastructure and workforce systems after the 2026 Digital Omnibus amendments.

A practical system-boundary method for deciding what must remain deterministic and local, what belongs at the edge, and what the cloud should do across an industrial AI fleet.

A prototype proves that a technical path can work. A product must prove customer value, system behaviour, field acceptance, ownership and repeatable economics together.

Edge deployment is not a hardware shortcut. It is a product decision about latency, offline behaviour, evidence, operating ownership and how a model changes after installation.

A product decision framework for separating the parts that differentiate your Physical AI product from the parts you should source, partner for, or leave alone.

An SDV platform is not a technology inventory. It is a set of product boundaries: what stays shared, what varies by vehicle, who can change it, and how every change reaches the field safely.