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Service · Computer Vision

Cameras that see
what your team can't

AI-powered visual inspection deployed on your existing cameras — detecting defects, counting objects, and monitoring operations 24/7 with accuracy human inspectors can't match at scale.

Manufacturing QC
Retail & Warehouse
Works on Existing Cameras
99.7%
detection accuracy — vs 94% average for human inspectors
<50ms
processing time per frame — real-time at any production speed
24/7
uninterrupted monitoring — no fatigue, no shift changes
4 wks
average time from camera assessment to live deployment
Live Demo

What an AI vision system looks like in action

This simulates a real detection feed from a factory quality-control line. Green boxes are passing units. Red indicates a detected defect.

SCOOP_VISION_v2 · CAM-01 · LINE-A
LIVE
UNIT_A · 99.2%
DEFECT · 97.8%
UNIT_B · 98.5%
CHECKING...
CAM-01 · 2026-05-18 · 09:14:22
30 FPS ●
Detections
UNIT_A
conf: 99.2%
PASS
DEFECT · scratch
conf: 97.8%
REJECT
UNIT_B
conf: 98.5%
PASS
UNIT_C
conf: 61.3%
REVIEW
Session stats
847
PASSED
12
REJECTED
98.6%
YIELD
3
REVIEW
CAM-01 · LINE-A
CAM-02 · LINE-B
CAM-03 · ENTRY
CAM-04 · PAKCING
Use Cases

Where AI vision makes the biggest difference

Any process that relies on a human eye looking at the same thing repeatedly is a candidate for automation.

Defect Detection
Inspects every product on the line for cracks, scratches, discolouration, dimensional errors, or missing components — at production speed, without slowing throughput.
Inventory & Stock Counting
Counts items on shelves, pallets, or conveyor belts in real time. Flags low stock automatically and syncs counts to your inventory system — no manual stock-takes required.
People Counting & Flow
Tracks visitor and staff movement across zones — store sections, production areas, or access points. Identifies crowding and optimises staff allocation based on live footfall data.
PPE & Safety Compliance
Detects whether workers on the floor are wearing required safety equipment — helmets, vests, gloves — and triggers an alert instantly when a violation is detected.
Security & Access Monitoring
Detects unauthorised access, loitering in restricted areas, or suspicious behaviour across your camera network — and sends real-time alerts to your security team.
Assembly Verification
Confirms that each assembly step was completed correctly — right components, right orientation, right sequence — before the product moves to the next station.
Label & Packaging Check
Verifies that labels are present, correctly positioned, not blurred, and match the expected product — catching mislabelling before goods leave the warehouse.
Freshness & Condition Detection
Used in food production and cold-chain logistics to assess product appearance, detect spoilage indicators, and flag batches that don't meet quality standards before shipment.
What's Included

Everything needed to run a production-grade vision system

Camera Assessment & SelectionWe audit your existing cameras and site layout. If new cameras are needed, we specify the right hardware — resolution, frame rate, lens, and placement — for your detection requirements.
Custom Model TrainingWe collect sample images from your specific environment and train a detection model on your actual products or defect types — not a generic model that guesses.
Real-time Alert SystemWhen a defect, violation, or anomaly is detected, the system triggers an instant alert — via dashboard notification, WhatsApp, email, or direct signal to your line PLC.
Analytics DashboardSee defect rates, yield per shift, detection trends, and camera health — all in one live dashboard. Exportable reports for QC managers and operations leads.
Multi-Camera SupportA single system manages multiple cameras across multiple production lines or zones. Each camera is configured independently, all feeding into one management interface.
ERP & MES IntegrationConnects to your existing manufacturing or warehouse systems — SAP, Odoo, custom databases — to automatically log detections, update quality records, and trigger downstream actions.
Edge or Cloud DeploymentRuns on-premise on an edge device (NVIDIA Jetson or equivalent) for low-latency processing without internet dependency — or cloud-hosted for multi-site deployments.
3-Month Post-Launch SupportWe monitor model accuracy, retrain on new defect types as your product evolves, and fine-tune thresholds to reduce false positives. Your yield numbers keep improving after launch.
How It Works

From camera assessment to live detection in 4 weeks

01
Week 1
Site assessment & data capture
We visit the site, assess lighting conditions, camera positions, and product flow. We collect a sample dataset of images covering normal units, known defect types, and edge cases — the quality of this data directly determines detection accuracy.
02
Week 1–2
Model training & validation
We annotate the dataset, train the detection model, and validate it against a held-out test set. Accuracy, precision, and recall are reported before anything is deployed. We don't move forward until the numbers meet the agreed acceptance criteria.
03
Week 2–3
Deployment & integration
The system is deployed on your hardware or cloud environment, integrated with your existing line or warehouse systems, and tested in production conditions. Alert routing, dashboard access, and reporting are all configured before handover.
04
Week 3–4
Go live + continuous improvement
The system goes live. Your team gets access to the dashboard and alert configuration. We monitor false positive and false negative rates for the first month and retrain on new edge cases as they appear in production — accuracy typically improves 2–3% in the first 30 days.
Manual vs AI Vision

What changes when you add AI eyes to your line

Capability Human inspector With Scoop Vision AI
Inspection speed Limited by human reaction time — typically 5–8 units/min Processes every unit at full line speed — no bottleneck ✓
Accuracy over an 8-hour shift Drops to 70–80% by hour 6 due to fatigue Consistent 99%+ — no fatigue, no variation ✓
Night shift coverage Overtime cost, lower morale, higher miss rate Same performance at 2am as at 9am ✓
New defect type response Retraining takes weeks — inconsistent until then Retrain the model in days — deployed without retraining staff ✓
Cost at scale Linear — more volume means more inspectors Fixed infrastructure cost — scales to 10x volume with no extra headcount ✓
Audit trail ✗ No automatic record of each inspection decision Every inspection logged with timestamp, image, and confidence score ✓
Technology

Built on the most reliable computer vision stack available

YOLOv8 / YOLO11
TensorFlow / PyTorch
OpenCV
NVIDIA CUDA / TensorRT
NVIDIA Jetson (Edge AI)
AWS Rekognition / GCP Vision
Roboflow / Label Studio
Python / FastAPI
RTSP / ONVIF Camera Protocol
MQTT / REST API / OPC-UA
"
Before the AI system, our QC team was manually checking every unit on two production lines — and we were still shipping a defect rate of 1.8%. Three months after deployment, the defect rate is 0.2% and our QC team has been redeployed to process improvement work instead of repetitive inspection. The ROI was clear in the first month.
HR
Hendra Rahardjo
VP Operations · PT Mitra Komponen Industri · Bekasi
Start today

Ready to let AI eyes
watch your production line?

We'll assess your current setup, identify the highest-value detection use case, and give you a scope and accuracy estimate — before you commit to anything.

Book a free vision assessment See case studies
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