テンプレート
戦略
Worked Product System Evidence Pack
Inspect a fictional cobot decision, evidence gaps and linked Product System exports. Modeled inputs and accountable roles remain distinct from recorded human approval.
Physical AIのための意思決定ツール
リソースライブラリ
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。
Learning Tracks
Four decision-led paths through the category, commercial opportunity, productization and transitional product leadership.
Category & Product System
Start with the discipline, ownership boundary and operating method before choosing tools or an engagement.
- 01Physical AI Product Management
- 02Physical AI Product Manager Role Charter
- 03Hyperion Physical AI Product System
- 04Worked Product System Evidence Pack
- 05Physical AI Product Management 2027: Design Protocol · No Findings
- 06Operated Product Decision Graph · HC-OPDG-001 v0.1.0 released
- 07Physical AI vs Operational AI: A Taxonomy
- 08Compare the Three Engagements
Opportunity & Commercial Viability
Test whether a technically promising system has a valuable problem, an economic buyer, a workable product boundary and a defensible investment case.
Prototype to Dependable Product
Turn a successful demonstration into a repeatable product by defining the next evidence gate across value, economics, system, safety and field operations.
Fractional Leadership & Transition
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
19
テンプレート
戦略
Physical AI Lifecycle Economics Stress Test
Compare a fictional baseline with five declared stresses using the existing simulator. Inspect capital, support, recovery and payback assumptions; these are modeled scenarios, not forecasts.
チェックリスト
戦略
Intralogistics Procurement Evidence Checklist
Sixteen evidence requests before a fleet commitment, with accountable roles, acceptance questions and hold conditions. Every request starts unassessed; no supplier score or approval is implied.
ガイド
戦略
Physical AI Product Management 2027: Research Protocol
The proposed study question, exploratory design, sample targets, consent boundaries and pre-launch gates. Recruitment has not started and no findings exist.
ガイド
技術
Deploying Mistral On-Prem for Manufacturing — Sovereign, Air-Gapped AI
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
Mistral vs OpenAI vs Anthropic for Industrial & Sovereign AI
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
Predictive Maintenance AI for Industrial Equipment: Condition Monitoring, RUL & ROI
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
AI Visual Quality Inspection for Manufacturing: Computer-Vision Defect Detection
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
Sim-to-Real for Industrial Robotics: Evidence Before Release
A product-decision guide to simulation assumptions, transfer tests, target hardware, policy authority, independent checks, recovery and field evidence. Compares simulator and policy choices without treating simulation performance as release proof.
ガイド
機能安全
Edge AI Under Functional Safety: Deploying AI in ISO 26262 & IEC 62443 Systems
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
Digital Twin ROI Playbook: From OPC-UA to a Production Twin
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
AI for Aerospace & Defence-Adjacent Manufacturing: A Civil-First, Dual-Use Primer
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
Auralink: A Physical AI Research Reference Architecture
A Hyperion-authored Physical AI research reference for EV-charging infrastructure, evaluated in simulation and bounded environments—not a live deployment or client-outcome claim.
ガイド
戦略
Physical AI Product Teardowns
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
The Prototype-to-Product Decision Gate
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
Physical AI Product Management: The Complete Guide
The operating reference for AI products that perceive, decide and act: strategy, discovery, productization, evidence, operating envelopes, safety, platforms, launch, field operations, metrics and scale.
ガイド
戦略
Physical AI Product Manager: Role Charter & Hiring Scorecard
A practical role definition for Physical AI product leaders: decision accountabilities, specialist boundaries, seniority ladder, hiring scorecard, interview loop, first-90-days plan and copyable role brief.
- メールで無料
テンプレート
機能安全
Safety-Case Evidence Template (ISO 26262 / DO-178C / IEC 61508)
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.
- メールで無料
テンプレート
規格
Edge AI Vendor Evaluation Matrix
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.
リソースの先に、意思決定があります。
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。