Counting that never loses track

Pharmaceutical Vial Counting at the Loading Station

Refrigerator Panel Hole Counting

Missing holes, blocked openings and incorrect panel variants can be missed when the inspection relies on a quick visual check.

Pallet SKU Counting and Verification

A visible pallet face does not necessarily reveal every carton or SKU. Counting needs an explicit boundary between visible evidence and hidden contents.

Multi-Lane Biscuit Counting with One Camera

A single camera above the post-oven conveyor replaces per-lane photoelectric sensors, using instance segmentation to count biscuits, flag broken or deformed pieces, and track lane balance across 2–3 lanes — addressing the specific failure mode that causes beam sensors to undercount once biscuits touch or shingle.

Bottle Cap Counting with In-Line Defect Detection

A single overhead AI camera counts bottles and screens caps for missing, skewed, wrong-colour, and cracked defects in one inference pass — building the kind of timestamped, batch-linked image record that FSSAI's revised packaged-water testing regime is moving toward, instead of relying on a torque sensor and a fatigued spot-check.

Automated Part Counting for Mixed-SKU Electronics Using Machine Vision and Deep Learning

An instance-segmentation machine vision architecture for counting mixed-SKU electronics and interconnect parts — from sub-5 mm micro-pins to 200 mm cable assemblies — in a single top-down image, built to remove the fatigue-driven error floor that published human-factors research documents in manual counting.

Dense-Pack Tray Counting for Tobacco Manufacturing Traceability

Vision-based count verification and SKU traceability for cigarette buds — replacing an assumed tray-fill count with a measured, image-backed, and audit-ready result at every tray, built around the count-tolerance and cycle-time targets a pilot actually validates rather than a number quoted off a spec sheet.

Pharmaceutical Vial Counting at the Loading Station

Refrigerator Panel Hole Counting

Missing holes, blocked openings and incorrect panel variants can be missed when the inspection relies on a quick visual check.

Pallet SKU Counting and Verification

A visible pallet face does not necessarily reveal every carton or SKU. Counting needs an explicit boundary between visible evidence and hidden contents.

Multi-Lane Biscuit Counting with One Camera

A single camera above the post-oven conveyor replaces per-lane photoelectric sensors, using instance segmentation to count biscuits, flag broken or deformed pieces, and track lane balance across 2–3 lanes — addressing the specific failure mode that causes beam sensors to undercount once biscuits touch or shingle.

Bottle Cap Counting with In-Line Defect Detection

A single overhead AI camera counts bottles and screens caps for missing, skewed, wrong-colour, and cracked defects in one inference pass — building the kind of timestamped, batch-linked image record that FSSAI's revised packaged-water testing regime is moving toward, instead of relying on a torque sensor and a fatigued spot-check.

Automated Part Counting for Mixed-SKU Electronics Using Machine Vision and Deep Learning

An instance-segmentation machine vision architecture for counting mixed-SKU electronics and interconnect parts — from sub-5 mm micro-pins to 200 mm cable assemblies — in a single top-down image, built to remove the fatigue-driven error floor that published human-factors research documents in manual counting.

Dense-Pack Tray Counting for Tobacco Manufacturing Traceability

Vision-based count verification and SKU traceability for cigarette buds — replacing an assumed tray-fill count with a measured, image-backed, and audit-ready result at every tray, built around the count-tolerance and cycle-time targets a pilot actually validates rather than a number quoted off a spec sheet.