From inbound material to finished product to field-return, we benchmark your quality system, then engineer a unified AI-powered quality fabric — vision inspection, statistical process control, root-cause discovery, and closed-loop adjustment — designed for zero-defect aspiration and audit-ready evidence.
Most quality systems are fragmented — vision in inspection cells, SPC in MES, complaint data in CRM, lab results in LIMS. Our Digital TQM practice unifies these into a single quality fabric: every defect, every measurement, every complaint linked back to the lot, the line, the operator, the supplier, and the conditions that produced it.
For variable processes — extrusion, coating, machining, baking — we close the loop: vision and gauge feedback drives continuous parameter adjustment, holding tolerance with a fraction of the variation of human-tuned operation. The result is fewer rejects, less rework, and a quality system that gets better with every shift.
A working catalogue of the systems, models, and platforms our engineers ship within this practice — selected for industrial reliability, observability, and scale.
Defect detection, surface, dimensional, and assembly verification at line speed using deep-learning CV.
Modern SPC with multivariate control, dynamic limits, and change-point detection on streaming data.
AI-driven RCA across thousands of process parameters surfaces non-obvious defect drivers human teams miss.
Real-time setpoint adjustment using vision and gauge feedback to hold tolerance on variable processes.
End-to-end product genealogy linking raw material, process, finished good, and field outcome.
Inbound quality intelligence, certificate-of-analysis automation, and supplier scorecards driven by performance data.
Representative deployments across our industrial client base. Each grounded in production engineering — not concept slides.
Line-scan cameras + CNN models on paper, foil, film, and steel coil lines detect scratches, holes, inclusions, color deviations at full line speed with continual learning.
Multi-camera stereo and structured-light systems verify presence, orientation, and dimensions of assemblies — replacing manual final inspection.
Modern multivariate control across hundreds of parameters with automated change-point detection — flags subtle drift weeks before classical SPC limits trigger.
Linking field-failure patterns back to manufacturing genealogy reveals which combinations of supplier, line, shift, and parameters produce failure-prone units.
Inline thickness measurement feeds RL controllers that adjust application rate in real time, maintaining tolerance with a fraction of legacy PID variation.
Vision and spectral analysis at goods-receipt detects out-of-spec inbound materials, automatically holds lots, feeds supplier scorecards in real time.
Generative-AI assistants help QC chemists triage out-of-trend results, draft investigations, and flag drift across lots, reducing release-cycle time.
Auto-compiled batch records, deviation reports, and CAPA files with AI drafting assistance — designed to pass FDA, EMA, and ISO audits.
Tools, frameworks, and platforms our engineers use day-to-day in this practice.
Every Pentaaxis engagement starts with a structured benchmarking conversation — no obligation, a senior engineer in the room, and a calibrated view of where AI moves the needle for your organization.