AI Process Advisor is the beta process-advisory layer in acqSYS. After each spool it reads the telemetry, QC results, and recipe context behind it and suggests a setpoint adjustment. A technologist decides what to try, the line receives it as a reviewed job comment — never raw AI output — and outcome tracking measures what actually changed.
The demo shows today's beta decision support clearly separated from the roadmap toward autonomous process setup and closed-loop optimization.
![Predicted Cpk 1.19 with confidence band [0.89, 1.48].](/assets/stage/suggestion-cpk.png)
Built for process review — the beta is not an operator-facing autonomous controller.
Scoring runs after each spool stop once the beta is switched on for your deployment.
Review a suggested adjustment — its evidence, its missing signals, and how the outcome is tracked.
Filament process improvement needs telemetry, QC, product, line, job, and spool context. A useful AI layer should show what it knows, what is missing, what action is controllable, and how the result will be reviewed.
Each suggestion arrives with its evidence — predicted Cpk, confidence band, missing inputs, and the setpoints a technologist can actually change.
Technologists, QA, managers, and admins review each suggestion with its spool, line, job, product, and setpoint context.
Technologists record try, skip, or not applicable — and every decision gets outcome tracking.
Toward autonomous process setup, closed-loop process control, production optimization, and formulation R&D automation.
The beta keeps a clear boundary between recommendation, human decision, operator-safe communication, and measured outcome.
AI begins from production evidence. The advisor depends on telemetry, QC, product, line, job, and spool context already present in acqSYS.
The boundary stays explicit: the current beta supports decisions — it does not silently control equipment or expose AI-origin wording to operators. Closed-loop optimization stays on the roadmap.
The advisor sits on top of the spool, QC, telemetry, ProductHub, job, line, operator-comment, and outcome records that acqSYS already connects.
Use AI before production to turn selected demand into scheduler-validated planning scenarios.
Explore →Supply the process signals behind advisor evidence.
Explore →Use QC outcomes as the review target.
Explore →Track performance and quality trends after decisions.
Explore →We will review your telemetry coverage, QC history, controllable setpoints, technologist workflow, and roadmap appetite for closed-loop optimization.
