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Service / Computer Vision

Watch what humans miss. At edge speed. At any scale.

Object detection, segmentation, OCR, defect detection, retail analytics, and safety monitoring. From YOLO on edge devices to custom HuggingFace pipelines on GPU clusters. We label, train, deploy, and monitor in production.

What we build

The menu.

Specific things we deliver under this service. Most projects combine three or four of these into one system.

/01

Object detection

YOLOv8, YOLOv9, RT DETR for real time detection. Custom datasets, custom classes, optimized for your hardware budget.

/02

Defect and quality inspection

Manufacturing line vision for surface defects, missing components, dimensional checks, and assembly verification.

/03

OCR and document AI

Custom OCR pipelines for IDs, invoices, forms, and handwritten records. Layout aware extraction with structured output.

/04

Retail and footfall analytics

People counting, dwell time, queue analytics, demographics, heatmaps. All edge inference. No cloud sensitive data.

/05

Safety and PPE monitoring

Hard hat detection, fall detection, exclusion zone monitoring, vehicle and pedestrian conflict alerts.

/06

Custom CV pipelines

Whatever your camera sees, we can model. Segmentation, classification, tracking, pose estimation, multi camera coordination.

Where it fits

Common use cases.

If any of these match your current operations, this service is probably the right entry point.

Warehouse and logisticsManufacturing QARetail storesConstruction sitesSmart cities and trafficAgriculture and farmingHealthcare imaging adjunctsInsurance damage assessmentDocument processingSports analytics
Spec

Technical parameters.

DetectionYOLOv8, YOLOv9, RT DETR, Detectron2, DETR variants
SegmentationSAM, SAM2, Mask R CNN, custom U Net
OCRTesseract, PaddleOCR, custom transformer based
Edge inferenceONNX, TensorRT, OpenVINO, CoreML
HardwareJetson, Coral, RPi, x86 industrial, AWS Panorama
LabelingCVAT, Roboflow, Label Studio, custom pipelines
TrainingCustom datasets, augmentation, active learning
Pricing$15k to $80k depending on hardware and accuracy
FAQ

Common questions.

How much labeled data do we need? +

Less than you think. With modern foundation models and pretrained backbones, 200 to 500 labeled examples is often enough for a strong v1. We use active learning to grow the dataset efficiently.

Cloud or edge? +

Depends on latency, bandwidth, and privacy. We have shipped both. Edge wins for low latency, offline robustness, and privacy. Cloud wins for centralized fleets and post processing analytics.

What hardware do you support? +

NVIDIA Jetson, Google Coral, Raspberry Pi, industrial x86, and standard cloud GPUs. We optimize for the cheapest hardware that meets latency and accuracy targets.

How do you handle false positives? +

Threshold tuning, multi frame confirmation, ensemble voting, and human in the loop review for borderline cases. We tune the cost asymmetry per use case. Missing a defect is worse than flagging one extra.

Can you integrate with our existing camera infrastructure? +

Usually yes. We work with IP cameras over RTSP, USB cameras, industrial cameras over GigE, and most factory and warehouse setups.

Ready to scope?

Bring the use case.

First call is 30 minutes. We will tell you whether this service is the right fit, and if not, which one is.