Decision tools for Physical AI
Field notes, evaluation frameworks and executive templates for turning industrial AI pilots into products that can be deployed, operated and trusted.
Four decision-led paths through productization, manufacturing systems, safety and sovereign architecture.
Move from a technical demonstration to a bounded product: system architecture, field evidence, sim-to-real transfer, safety constraints and an inspectable reference implementation.
Trace the full production system around an industrial model: sensing, inspection, equipment health, OT integration, deployment economics and sovereign compute.
Define intended use, runtime authority, safety evidence, cyber boundaries and regulatory obligations before architecture and procurement harden.
Make the edge, model, data and supplier boundaries explicit—then choose an architecture that can be operated, changed and defended over its full lifecycle.
Source-grounded analysis
The applicable date depends on how the system is classified. Read the current analysis, then map intended use, system boundaries and obligations before treating any date as your deadline.
All Resources
14
Guides
Technical
How manufacturers deploy Mistral AI on-premise and in air-gapped environments. Covers the Sovereign Model Ladder, Mistral Forge/Studio/Compute stack, use cases across aerospace, automotive, semiconductors, and energy, and EU AI Act compliance.
Guides
Technical
Honest side-by-side comparison of Mistral, OpenAI GPT, and Anthropic Claude for industrial and sovereign AI workloads. Covers data residency, on-prem deployment, fine-tunability, cost at scale, capability ceiling, vendor lock-in, and EU AI Act fit — plus when frontier models genuinely win.
Guides
Technical
How to build predictive maintenance for production equipment: the data foundation (vibration, thermal, and motor-current signatures over OPC-UA and time-series), modeling approaches (anomaly detection, remaining-useful-life, survival models), edge vs cloud inference, CMMS/SCADA integration, and how to quantify ROI (downtime avoided, MTBF). Framed against ISO 13374 condition monitoring and IEC 62443 OT security. Ties to the live CSV-maintenance demo on this site.
Guides
Standards
How to deploy computer vision quality inspection on the production line: surface defects, assembly/completeness, and weld inspection — sensor and lighting setup, dataset and annotation strategy, and edge deployment. Includes the honesty boundary: a vision model surfaces candidate indications mapped to a vocabulary like ISO 5817 weld imperfections — it does not assign a certified grade (that needs metrology and your WPS). Ties to the live plant-audit and defect demos on this site.
Guides
Technical
How robotics integrators, AMR/AGV vendors, and ROS 2 teams close the sim-to-real gap: physics simulation, domain randomization, synthetic data, sim-to-real transfer, virtual commissioning, and on-robot edge inference. Covers Isaac Sim, Gazebo, MuJoCo, VLA policies, and ISO 10218 / ISO TS 15066 / IEC 61508 safety.
Guides
Safety
Deploying AI in safety-critical embedded systems: ISO 26262 ASIL levels, SOTIF (ISO 21448), IEC 61508 SIL, IEC 62443 OT cybersecurity, runtime assurance monitors, operational design domains, and the edge inference toolchain (ONNX/TensorRT).
Guides
Technical
A practical ROI framework for industrial digital-twin programmes: the data foundation (PLC → OPC-UA → time-series → twin), the five-rung maturity ladder, where AI enters (anomaly detection, predictive maintenance, process optimization), how to quantify ROI, and build-vs-buy guidance for manufacturing leaders.
Guides
Standards
How MRO operators, avionics suppliers, and UAV makers deploy AI for predictive maintenance, automated NDT, and documentation copilots — with a clear-eyed view of DO-178C / DO-254 / ARP4754A certification, EASA's learning-assurance roadmap, and the sovereign on-prem case. Civil-first; no defence contracts or clearances.
Guides
Standards
A Hyperion-owned, pre-production Physical AI reference implementation for EV-charging infrastructure: evaluated in simulation and bounded environments, not deployed in live infrastructure. Research boundary: 78% autonomous incident resolution in a controlled-environment evaluation reported in arXiv preprint 2603.08736.
Guides
Strategy
Three shipped physical-AI product families read for the decisions behind them — EV charging reliability, Android Automotive inside a homologated vehicle, and video platforms at 100M+ scale. Public observation, not client case studies.
Guides
Strategy
Five decisions that determine whether a Physical AI prototype becomes a product — degraded-mode ownership, variant count, field survival, the scale delta, and who signs the compliance case. Each gate carries the test that answers it and the failure it prevents.
Guides
Strategy
A direct definition of the discipline, how it differs from software product management and systems engineering, and the five-stage, six-lens Product System used to move from prototype to a dependable product.
Templates
Safety
A practitioner template for structuring an AI safety case: the Claims–Arguments–Evidence skeleton, a HARA input sheet, ASIL/DAL/SIL decomposition placeholders, and a V&V traceability matrix — mapped to ISO 26262, DO-178C and IEC 61508. It is a template and checklist; a notified/certification body assigns the actual rating, we engineer the evidence.
Templates
Standards
A vendor-neutral scoring matrix for evaluating edge-AI inference hardware and vendors: TOPS/W, P99 latency, ONNX/TensorRT-class format support, on-prem/sovereign deployment, toolchain maturity, functional-safety support, power/thermal envelope, and long-term support — with a weighted 1–5 rubric. Criteria and a scoring method, not a ranked vendor list.
Use the library to frame the system. Bring the unresolved boundary — product, evidence, safety, architecture or economics — to a focused fit call.