Physical AIのための意思決定ツール
産業AIのパイロットを、展開・運用でき、信頼される製品へ変えるためのフィールドノート、評価フレームワーク、経営層向けテンプレートです。
Four decision-led paths through the category, commercial opportunity, productization and transitional product leadership.
Start with the discipline, ownership boundary and operating method before choosing tools or an engagement.
Test whether a technically promising system has a valuable problem, an economic buyer, a workable product boundary and a defensible investment case.
Turn a successful demonstration into a repeatable product by defining the next evidence gate across value, economics, system, safety and field operations.
Choose the right temporary leadership mode, write the decision rights and cadence, then build the successor and exit into the mandate from day one.
すべてのリソース
14
ガイド
技術
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.
ガイド
技術
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.
ガイド
技術
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.
ガイド
規格
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.
ガイド
技術
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.
ガイド
機能安全
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).
ガイド
技術
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.
ガイド
規格
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.
ガイド
規格
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.
ガイド
戦略
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.
ガイド
戦略
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.
ガイド
戦略
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.
テンプレート
機能安全
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.
テンプレート
規格
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.
プロダクト、行き詰まっている意思決定、その期限、エグゼクティブスポンサー、そして現在あるエビデンスをお聞かせください。Hyperion が適切なマンデートかどうか — そうでない場合も含めて — Mohammed が率直にお伝えします。