Industrial AI built on real operational data.
AI strategy and data platforms for discrete and process manufacturers. Predictive maintenance, quality control ML, supply chain analytics, and the OT/IT data integration required to make factory-floor AI actually work in production environments.
Manufacturing AI projects often stall not because the models are wrong, but because the underlying data architecture cannot support them. Sensor data from PLCs and SCADA systems, quality data from MES platforms, ERP data, and supply chain data live in separate worlds with incompatible formats, time resolutions, and ownership boundaries.
Yearling Solutions brings engineers who understand both the OT environment where manufacturing data originates and the cloud data platforms where it needs to land for ML and analytics. We design data integration architectures that bridge the plant floor and the cloud, and we build ML systems that integrate back into operational workflows rather than living in dashboards nobody reads.
Standards & Industry Context
The standards and operational frameworks that shape AI and data programs in manufacturing.
ISA-95 / ISA-88
Manufacturing operations management and batch control standards that define data exchange between enterprise and manufacturing systems.
OPC-UA
Industrial connectivity standard for real-time sensor data collection from PLCs, SCADA, and industrial equipment.
ISO 9001 Quality Management
Quality management system requirements and the data obligations for process control, nonconformance, and corrective action tracking.
IATF 16949 (Automotive)
Automotive quality management requirements including statistical process control data and measurement system analysis.
NIST Cybersecurity for OT
Security considerations for OT/IT data integration and the control environment implications of connecting plant-floor systems to cloud platforms.
Supply Chain Data Standards
GS1, EDI, and supplier data exchange standards relevant to supply chain analytics and demand forecasting systems.
What We're Seeing
The data and AI challenges driving conversations with manufacturing technology leaders today.
Sensor data that never reaches analytics systems
Most manufacturers collect enormous volumes of equipment sensor data that is either discarded or stored in isolated historian databases. Connecting historian data to cloud analytics is where most manufacturing AI programs start and stall.
Quality data in disconnected systems
Quality data lives in MES systems, paper records, CMM outputs, and ERP nonconformance modules that were never designed to talk to each other. ML quality models require clean, integrated data before they can identify patterns.
Demand forecasting accuracy that does not justify the investment
Many manufacturers have tried statistical forecasting tools that produced marginal improvements over planners' intuition. ML-based forecasting delivers larger gains when it is trained on clean historical data with proper feature engineering for their specific product mix.
OT/IT integration complexity blocking AI use cases
Plant operations teams are rightly cautious about connecting production equipment to cloud platforms. Industrial AI requires a data architecture that extracts value from OT data without creating security or operational risk.
How We Help
Production AI and data engineering for manufacturing organizations, built for the operational environment.
Predictive Maintenance & Asset Intelligence
- Vibration, temperature, and process sensor anomaly detection models
- Remaining useful life prediction for critical equipment
- Historian data integration (OSIsoft PI, AspenTech, Ignition) to cloud
- Maintenance work order integration and alert routing automation
Quality Control & Process Optimization
- Statistical process control ML augmentation and drift detection
- Computer vision inspection systems for defect detection
- Root cause analysis models for nonconformance and scrap reduction
- MES and CMM data integration for end-to-end quality analytics
Supply Chain & Demand Analytics
- Demand forecasting models with external signal integration
- Supplier performance analytics and risk scoring
- Inventory optimization and reorder point ML
- ERP and supplier EDI data integration pipelines
OT/IT Data Integration
- OPC-UA and MQTT data collection and cloud ingestion architecture
- Edge computing design for real-time inference at the plant floor
- Secure data extraction architecture that preserves OT network isolation
- Unified data platform design spanning ERP, MES, historian, and supply chain
Perfect For
Manufacturers building production AI and data capabilities across plant operations and supply chain.
Discrete manufacturers building predictive maintenance programs on existing sensor infrastructure
Process manufacturers integrating historian data with cloud analytics platforms for yield optimization
Automotive suppliers building quality ML systems on SPC and CMM data
Distribution and contract manufacturers improving demand forecasting accuracy for complex product mixes
Industrial companies replacing manual quality inspection with computer vision systems
Manufacturers with multi-site ERP deployments seeking unified supply chain analytics
Proof in Manufacturing
Real engagements with measurable outcomes.
Heavy equipment dealer advances operational resilience through cyber assessment
Independent review of perimeter, segmentation, and detection across distributed operations. The same assessment discipline we apply to OT/IT integration architecture before connecting plant systems to cloud platforms.
Read case studyAI & Data EngineeringLLM benchmarking framework accelerates enterprise AI adoption
Structured model evaluation that removes guesswork from AI selection. The same rigor applied to industrial AI use cases where production reliability requirements are non-negotiable.
Read case studyData EngineeringEnterprise data retrieval system improves analyst productivity
Semantic search over unstructured enterprise data. The same retrieval architecture powers maintenance documentation search, quality records retrieval, and supplier intelligence for manufacturing teams.
Read case studyPair AI with OT Security
Cybersecurity for Manufacturing
Pair your AI and data program with OT/ICS security controls that protect plant operations as you connect systems to the cloud.
Ready to put your manufacturing data to work?
Talk with engineers who understand OT environments, industrial data formats, and the integration challenges that separate manufacturing AI pilots from production systems.