Lifecycle stage — Ship
Your AI pilot is working — real sensors, real inference, real feedback from operations. The next commitment it has to carry is bigger: a production rollout to a full robotics cell, an AGV fleet, a vehicle program, or a multi-site energy grid deployment. Each of these exposes gaps the pilot could tolerate — latency spikes that exceed the PLC scan cycle, model-accuracy degradation under sensor drift, a safety envelope that was informally agreed in the lab but has never been documented for a certification engineer — and the production system cannot. This is the Ship stage of the Hyperion Lifecycle: a 12-week embedded engagement that takes a working edge or embedded AI pilot through readiness assessment, evaluation and observability, safety and compliance hardening, and scaling readiness on constrained hardware. I architected Auralink — 1.7M lines of production code, approximately 20 autonomous agents resolving 78% of incidents without human intervention, arXiv 2603.08736 — and have shipped 10 AI ventures to production including work on autonomous physical systems. The failure patterns for physical AI pilots repeat; the fixes are known; the sequence matters.
The latency profile that worked in the lab violates the operational SLA at production load. Your pilot ran one inference call per second on a development Jetson with no concurrent sessions. Production means eight robotics cells sharing one compute node, or forty AGVs hitting the fleet inference service simultaneously, or a vehicle ECU handling a safety-critical perception request within the PLC scan cycle budget. The first time you hit real concurrent load you discover whether the bottleneck is model serving, the OT network latency, the CAN bus timing budget, or the inference framework's threading model — and you discover it in front of the operations team whose approval the rollout depends on.
The safety envelope was informally agreed in the lab but has never been documented for a certification review. Your safety engineer informally accepted the pilot output for demonstration purposes. They have not accepted it for a production deployment. To move to production, you need a written hazard analysis, a failure mode coverage document, envelope violation test results, and a risk management record — none of which exist. The pilot stalls in a certification review that will take months if you start the documentation work from scratch when the production rollout is already scheduled.
Sensor drift and calibration variation cause model-accuracy collapse in the production environment. The pilot ran on freshly calibrated sensors in controlled conditions. Production means sensors that have been running for eighteen months with thermal drift, a firmware version the ML team did not validate against, and occasional electrical faults the maintenance team has learned to tolerate. Model accuracy collapses within days of the production rollout and the ops team cannot tell whether the model is broken, the sensors are broken, or the integration is broken.
The pilot has no AI-specific observability for constrained hardware. You have no latency distributions from the actual edge compute node under real load, no model-drift detection tuned to the sensor characteristics of your production environment, no cost-per-inference tracking on constrained hardware, and no alerting on the failure modes that matter in a physical system — missed detections, false positives that trigger actuator commands, inference timeout that falls back to a fail-safe mode. Every incident in the pilot-to-production gap becomes a forensics exercise that sets the rollout back weeks.
The engagement runs in four three-week phases. I work embedded with your team — your engineers build, I bring the readiness ranking, the physical-AI eval methodology, the safety-documentation sequence, and the edge-specific scaling tests. The goal is not to rebuild what works; the goal is to harden it into a system that clears the production rollout with evidence, not with hope.
Deep assessment across the physical-AI production-readiness dimensions: inference latency under realistic concurrent load on the target hardware, sensor-data quality in the production environment versus the pilot environment, safety-envelope documentation status versus what the certification review will require, observability coverage for edge-specific failure modes, and OT/IT integration completeness. Every gap ranked on four tiers: production-rollout blocker, safety-certification blocker, operational-stability risk, and hardening backlog. Each item gets an effort estimate and an owner suggestion.
An evaluation pipeline built for physical-AI: real-hardware inference benchmarks under realistic concurrent load, sensor-drift simulation to measure accuracy degradation bounds, failure mode injection tests (sensor fault, network partition, actuator command timeout), and regression test suites that run on the target edge hardware rather than in a cloud simulator. AI-specific observability for constrained hardware: per-inference latency distributions, sensor-drift detection, fail-safe transition logging, cost-per-inference tracking, and dashboards the operations team can read without ML training.
The safety documentation that the certification review requires and the pilot did not produce: written hazard analysis, failure mode coverage documentation, envelope violation test results, risk management record, and the Annex IV technical documentation if the system falls under EU AI Act high-risk classification (Annex III). For regulated environments, the evidence chain is built to the governing standard — ISO 26262 for automotive, IEC 61508 for industrial, DO-178C for airborne systems, ISO 10218 for robotics. OT cybersecurity documentation under IEC 62443 where required. Done right in three weeks; done wrong at the last minute it takes months.
Load testing at realistic production scale — full robotics cell, full AGV fleet, full vehicle program — on the target edge hardware with the real OT network in the loop. Bottleneck identification and remediation: inference framework threading, model quantization tier, batch-size strategy, CAN or OT network latency budget, compute-node thermal throttling. Documented trade-offs for the gaps we choose to accept at this rollout scale, with the signals the operations team should watch as the footprint grows. Rollout sequencing: cell-by-cell, vehicle-by-vehicle, or site-by-site expansion plan agreed with the safety engineer.
Manufacturers with a robotics cell or AGV pilot that works in the lab but cannot clear the safety-certification review or the OT-integration acceptance test for a full production rollout. Automotive OEMs and Tier-1 suppliers with an ADAS or AD pilot that meets the bench accuracy target but has never been load-tested on the vehicle ECU at realistic concurrent request rates. Energy utilities with a predictive AI pilot at one substation that has never been validated for a multi-site grid-edge rollout. Any operator where the pilot has real sensors, a production rollout is on the calendar, and the team knows the current system was not built for what is coming. This is not for teams whose pilot is a notebook or a cloud demo — those organisations need the Physical AI Deployment service or the Strategy Sprint first. It is also not for organisations without engineering capacity to embed with the engagement; the handoff model assumes a team that will own the system after week twelve.
Because the pilot was built for lab conditions: fresh sensors, single-session load, informal safety-envelope agreement, and tolerant reviewers. The production rollout multiplies concurrent load, introduces sensor drift and calibration variation, requires a documented safety case that will survive a formal review, and puts the system in front of operations engineers whose acceptance the rollout depends on. About a third of the pilots I assess in week one are closer to production-ready than the team thought — in those cases the engagement focuses on the specific gaps rather than the full program. I will tell you honestly in week three.
For a high-risk system under ISO 26262 ASIL-C or ASIL-D, or an airborne system under DO-178C, the safety documentation work can exceed three weeks. In those cases, the safety-hardening phase expands and I will scope that explicitly in week one. A system that requires a full ASIL-D decomposition and notified body review is a different scope from a system that requires a written hazard analysis and IEC 62443 zone documentation — and I will say so before the engagement starts, not after it has run for six weeks.
Yes. Your automation partner owns the PLC environment, the OT network, and the systems-integration layer. I own the AI-specific production readiness — inference performance on the target hardware, safety documentation, physical-AI observability, and scaling tests. We meet weekly so the work products reconcile, particularly on the OT-integration and safety-documentation sections where the automation partner's knowledge of the production environment is essential input.
For physical AI systems, EU AI Act scope is significant. Autonomous and industrial AI systems typically fall under Annex III high-risk categories. The safety and compliance hardening phase produces the Annex IV technical documentation and post-market monitoring plan at the level required by the risk classification. For systems that require a notified body review, that is a separate track that runs alongside this engagement; I scope that in week one based on your risk classification.
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