You've spent between €50k and €500k on AI tools, pilots, and vendor contracts over the past eighteen months. You cannot tell me — with numbers — whether any of it is working. For industrial operators, that gap is sharper: the AI may never reach the factory floor, the vehicle stack, or the substation if the OT/IT boundary is unmapped, sensor-data pipelines are unvalidated, and the safety regime (ISO 26262, IEC 61508, IEC 62443) has not been assessed. This is the Discover stage of the Hyperion Lifecycle, compressed into two weeks and priced as a flat fee. I've audited more than 30 AI initiatives and shipped 10 AI ventures including Auralink (1.7M lines of production code, arXiv 2603.08736) and work on autonomous physical systems. The failure patterns repeat. I know which gaps are blockers for physical deployment versus which ones you can address in a second wave.
The OT/IT boundary is unmapped and your AI cannot cross it safely. Industrial operators have production networks that were never designed for AI inference traffic — PROFINET rings, safety PLCs on isolated subnets, vehicle CAN buses with hard timing budgets. Your IT team has approved a cloud AI vendor. Your OT team has not. The two domains do not share a data governance framework, an incident response protocol, or a network security posture that a regulator under IEC 62443 would accept. An AI pilot that works in the IT zone stalls the moment it needs to read from a sensor or write to an actuator across the OT boundary.
Sensor-data pipelines have never been validated for AI training. The historian logs look complete. In practice, the thermal sensors on line 3 have a 200ms timestamp offset, the vibration data was collected at a different sample rate than the spec sheet states, and the labelling convention changed in 2022 without a migration record. A model trained on this data will have biases nobody can explain once it is in production. The data quality problem in industrial AI is systematic — it requires a dedicated audit pass, not a spot check.
Edge-inference readiness has not been assessed. Running a model on a Jetson, an Intel NUC, or an embedded ECU is a different engineering problem from running it in a cloud container. Quantization, latency budget, memory footprint, deterministic behavior under sensor fault, graceful degradation during network partition — none of these are documented in your current AI vendor's contract, and none of them have been tested on your actual production hardware.
Safety-regime maturity is unknown. Your sector almost certainly has a governing functional-safety or cybersecurity standard — ISO 26262 for automotive (ASIL), IEC 61508 for industrial (SIL), IEC 62443 for OT cybersecurity, ISO 10218 for robotics. Your AI pilot was not scoped against any of them. Before a physical AI system can move from pilot to production, a safety engineer needs evidence the AI audited team has not yet produced. This audit tells you exactly which evidence is missing and how long it will take to close the gap.
This is a flat-fee engagement with a fixed scope and a fixed deadline. Week one is discovery and technical assessment — OT/IT architecture, sensor pipelines, edge hardware, safety-regime documentation. Week two is synthesis and the written deliverable. I work asynchronously between interviews so your team is not blocked waiting on me.
Structured 60-minute interviews with the plant manager or VP Engineering, the OT/SCADA lead, the ML or data team lead, the safety or certification engineer, and two operations engineers who work the production environment daily. I also review your network architecture diagrams, your current AI pilot documentation, your vendor contracts, and any existing safety assessments or IEC 62443 / ISO 26262 gap analyses.
Deep assessment across four physical-AI dimensions: (1) OT/IT boundary — network segmentation, data-flow paths, IEC 62443 zone and conduit model; (2) sensor-data pipelines — timestamp integrity, sample-rate consistency, labelling provenance, historian data quality; (3) edge-inference readiness — target hardware assessment, latency and memory budget, quantization viability, connectivity partition behaviour; (4) safety-regime maturity — which standard applies, which artifacts exist, which are missing for a production deployment.
Every gap ranked on four tiers: physical-deployment blocker (the AI cannot move to production hardware without this), safety-regime blocker (a certification engineer will reject the deployment without this evidence), OT-integration blocker (the AI cannot read from or write to the operational stack without this), and hardening backlog (address before scaling from one site to five). Each item gets an effort estimate and an owner suggestion.
A 90-day roadmap — specific projects, owners, success metrics — sequenced so the OT/IT boundary work and the safety-evidence work proceed in parallel with any model improvement work. Delivered in a 90-minute readout to your leadership team. You leave with a document the engineering lead can execute and the safety officer can review.
Plant managers, VP Engineering and operations leaders at manufacturers, automotive OEMs, energy utilities, and infrastructure operators who have an AI pilot running in the IT zone but cannot get it across the OT boundary and onto production hardware. Safety or certification engineers who need an independent view of what evidence the ML team has not yet produced. CAIOs at industrial or automotive companies preparing a Q3 or Q4 commitment to physical-AI deployment and needing an outside assessment before signing the next vendor contract. This is not for pure SaaS companies with no physical operations — those organizations need the generic AI Readiness Audit track. It is also not for organizations without any AI deployed; the audit assumes something to assess.
Because your OT/IT team understands the network topology but has not assessed it through the lens of AI inference data flows, model update pipelines, and safety-regime evidence requirements. The audit is not a network security assessment — it is an assessment of whether your OT/IT architecture can support a physical AI deployment at production scale, and that requires someone who has shipped AI into physical systems before.
The assessment is scoped to the standards that govern your sector: ISO 26262 and SOTIF (ISO 21448) for automotive, IEC 61508 for industrial (SIL), IEC 62443 for OT cybersecurity, ISO 10218 for robotics, and EU AI Act Annex III for high-risk autonomous and industrial systems. The audit identifies which standard applies, what evidence currently exists, and what is missing for a production deployment. I do not conflate cyber standards (UNECE R155/R156) with functional-safety standards (ISO 26262/IEC 61508) — they govern different failure domains and require different evidence.
Then the report will say so, in writing, with the specific gaps documented. About one in four industrial AI audits concludes that the OT/IT integration work is the critical path — not the model — and that proceeding without addressing it will produce a failed deployment at significant cost. That finding is valuable: it prevents a 16-week physical deployment engagement from running into a structural blocker in week eight.
A vendor's readiness assessment is designed to qualify you for their next product sale. This audit is designed to tell you what your physical AI deployment actually requires — including findings that may recommend against a vendor contract renewal. I have no product to sell after the audit; the deliverable is the ranking.
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