A typical process data analytics engagement. A production line records tens of thousands of signals over time; quality is judged per product: per batch, roll or part.
Sensors, drives, cameras and labs record their data on separate time bases in separate systems. To link a defect to the process conditions that caused it, each measurement has to follow the material through the line, even when it is stretched, cut or turned on the way.
We map which signals exist, at which rates, and how the material changes along the line. Then we choose storage per data type: a time-series database for high-rate signals, a NoSQL store for product genealogy and quality records, image and video storage for inspection data, and a warehouse for reporting. The technical specification covers the data model, the tracking logic, the streaming architecture, retention and access.
We use agentic engineering: AI agents, under our engineers’ review, build the system. Data arrives through Kafka and message queues. Online processing, scripted code applied directly to the streams and queries, converts time-based signals into positions on each product while the material moves, and keeps tracking it through every step. Standard open-source components form the base; custom agentic-engineered solutions cover what they do not.
We connect the live line, validate the tracking against known products with your process and quality engineers, and run alongside your existing systems until the results hold.
Your engineers query by period, grade, customer or a single section of one product, without waiting for IT. Code, data and documentation are yours, together with the agents that keep the pipelines running, add signals when the line changes and write new stream and query scripts on request.
Process data, but no answers? Tell us the problem.
Contact us