Quality Intelligence Foundation Reference Architecture

Architecture Summary

This architecture shows how quality data is collected, contextualized, and transformed into real-time quality intelligence at the edge.

Litmus Edge connects production, vision, and lab systems, structures quality data, and runs AI models for low-latency inspection and anomaly detection. It enables defect classification, quality event creation, and response workflows directly on the plant floor.
Litmus Edge Manager supports centralized rollout and scaling
Optional integrations enable traceability, analytics, and enterprise quality system alignment.

End-to-end data flow

1.
Connect quality data sources
Litmus Edge connects to PLCs, vision systems, lab systems, quality stations, MES, and local applications to collect machine, process, and inspection data.
2.
Store and serve quality data
Store inspection events, defect history, and time-series data locally for quality workflows and analysis.
3.
Contextualize quality data
Raw quality signals are mapped to asset, line, product, recipe, batch, lot, and order context so defects and measurements can be traced back to production conditions.
4.
Run edge AI for quality
Run vision AI and ML models for low-latency inspection, anomaly detection, defect detection, and pass / fail classification.
5.
Create quality events and data products
Litmus generates structured pass / fail events, defect classifications, and reusable quality data products, and quality correlations that can be reused by downstream apps and analytics..
6.
Drive operational response
Alerts, operator guidance, and CAPA / NCR workflows help teams respond faster to quality issues and prevent repeat defects.
7.
Deliver trusted quality intelligence
Provide traceability, quality insights, vision AI outputs, and continuous improvement data across operations and enterprise systems.

Architecture Summary