Methodology demonstration: How we apply the Hyperion Lifecycle to triage stuck pilots and reach production in 12 weeks
This case study illustrates our Hyperion Lifecycle for rescuing stalled AI initiatives. The scenario reflects the pattern we see repeatedly in mid-market manufacturing: multiple stuck pilots, no MLOps foundation, and board pressure for ROI. Metrics and timeline represent typical outcomes. Client details anonymized.
Taille: Typical engagement: 200–2,000 employee manufacturers
Six AI pilots running for 18 months with no path to production — a classic pilot purgatory scenario we encounter frequently in manufacturing.
Six AI pilots running for 18 months with no path to production — substantial budget invested 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
Applied 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 standardized deployment patterns.
Audited 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 weeksBuilt 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.
2 weeksShipped 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 optimization across the factory floor.
8 weeksIllustrative scenario: Applying the Hyperion Lifecycle to go from 0 to 4 AI systems in production in 12 weeks, with significant operational savings.