From AI Pilot Graveyard to Production: 4 AI Systems Shipped in a Modelled 90-Day Scenario

About the Client

This case study illustrates my Hyperion Lifecycle for rescuing stalled AI initiatives. The scenario reflects the pattern I see repeatedly in mid-market manufacturing: multiple stuck pilots, no MLOps foundation, and board pressure for ROI. Metrics and timeline represent typical outcomes. Illustrative scenario, not a specific client engagement.

Size: Typical engagement: 200–2,000 employee manufacturers

The Challenge

Six AI pilots running for 18 months with no path to production — a classic pilot purgatory scenario I encounter frequently in manufacturing.

• Six AI pilots running for 18 months with no path to production — €800K spent with nothing to show
• No MLOps infrastructure: models were trained locally with no reproducibility, versioning, or deployment pipeline
• Severe data quality issues across factory floor sensors, ERP systems, and quality control databases
• No production architecture — pilots were built as Jupyter notebooks and Flask demos, not production systems
• Each pilot built by a different vendor with no integration plan, incompatible tech stacks, and siloed data
• Leadership needed to demonstrate AI ROI to the board within one quarter or risk losing the entire AI budget

My Solution

Used the Hyperion Lifecycle to audit, triage, and ship 4 AI systems to production in 12 weeks — killing 2 unviable pilots and building a shared MLOps foundation.

Systematic triage of all 6 pilots into 'ship', 'pivot', and 'kill' categories. Built a shared MLOps pipeline and data quality layer as the foundation, then shipped 4 production systems in parallel using standardised deployment patterns.

Implementation Phases

• 2 weeksAudit & TriageAudited all 6 pilots against production-readiness criteria. Triaged into 'ship' (4 pilots with viable models and clear ROI), and 'kill' (2 pilots with fundamental data or business case flaws). Honest assessment saved months of wasted effort.
• 2 weeksMLOps Foundation & Data QualityBuilt shared MLOps pipeline with CI/CD for models, model registry, and automated retraining triggers. Implemented data quality layer across factory sensors and ERP systems to ensure clean, reliable inputs.
• 8 weeksProduction Deployment — 4 AI SystemsShipped 4 systems to production in parallel: (1) Predictive maintenance for CNC machines, (2) Quality inspection with computer vision, (3) Demand forecasting integrated with ERP, (4) Energy optimisation across the factory floor.

Python · PyTorch · ONNX Runtime · MLflow · Kubernetes · Apache Kafka · PostgreSQL · Grafana · Prometheus · SAP Integration · NVIDIA Jetson (Edge) · Docker

Results & Impact

Illustrative scenario: applying the Hyperion Lifecycle to triage stuck pilots and move a subset toward production, with a shared MLOps foundation as the reusable outcome.

Services Delivered

Pilot to Production · Production AI Systems · Industrial AI

Want Results Like These?

Every engagement starts with a 30-minute diagnosis. Describe your situation, and I will tell you — honestly — whether I can help and how fast.