YieldLens is an AI-first predictive quality and scrap intelligence tool for small and mid-sized factories using legacy MES/Excel rather than big-ticket Industry 4.0 suites. Plant managers in plastics, machining, and food processing complain on manufacturing forums that they lack usable insight into which settings, materials, and operators drive defects. YieldLens connects to PLC logs, QC spreadsheets, and simple sensors to learn production patterns and forecast defect spikes before they happen, surfacing clear recommended setpoints or upstream fixes. - No-data-scientist setup: guided onboarding that maps machine tags and QC forms into a usable schema in days, not months. - Shift-aware insights: highlights which lines, shifts, and suppliers correlate with rework and warranty claims. - Actionable alerts: sends operators specific instructions (“reduce barrel temp by 5°C on Line 3 for Lot 8921”) rather than vague dashboards. - Revenue via tiered SaaS plus per-site deployment fees, with a durable moat from accumulated labeled defect/parameter datasets per process and vertical.