Product recognition
Challenge
Products have similar packaging and change between catalog versions.
Our approach
Annotate product instances and map visible attributes to the current catalog.
Introducing advanced AI data pipelines for Retail
Product and store data built for retail AI
Shelves, checkout counters and product catalogs need labels that capture real operational detail. Engai prepares retail datasets around your objects, scenes and review requirements.
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Retail Data Annotation
Expert Labeling of Retail
Data
Retail Data Annotation services for a wide range of computer vision applications.
0
completed projects
Smart Store & Checkout Vision
Retail · Autonomous Checkout & SKU Perception
0
annotated files
SKU & Planogram Accuracy
Smart retail and autonomous checkout systems rely heavily on accurate computer vision datasets to recognize products, prevent shrinkage, and verify shelf planograms in real time.
Engai delivers precision SKU bounding boxes, item interaction tracking, and shelf segmentation datasets that power seamless physical AI retail deployments worldwide.
Retail / Capabilities
Retail Core Solutions
Carefully annotated data for retail perception, automation and research.
Retail label taxonomy
Define classes for products, shelf regions and checkout items, with visual examples and explicit boundary rules.
Capture-aware quality review
Review the effects of lighting, scale and occlusion in shelves, checkout counters and product catalogs.
Model-ready annotation data
Connect retail labels to source identifiers, scene metadata and the export schema your model uses.
Annotation capabilities
Retail Data Services
Shelves, checkout counters and product catalogs need labels that capture real operational detail. Engai prepares retail datasets around your objects, scenes and review requirements.
Locate each visible object with an axis-aligned rectangle and a class label. Applied to products, shelf regions and checkout items.
Data Annotation for Retail
AI shelf monitoring
Label product facings, empty spaces and shelf boundaries for availability and planogram analysis.
Computer vision store analytics
Map display zones and observable shopper movement to support spatial analysis of store operations.
Checkout item recognition
Separate overlapping products into individual instances and connect them to a consistent product taxonomy.
Inventory condition inspection
Annotate damaged packaging, misplaced products and visible inventory discrepancies for review systems.
Retail AI assistants
Curate catalog attributes and reviewed product questions for search, support and store operations.
Client reviews
on 
Careful work on the small details
The sample batch helped us agree on how to handle overlapping leaves and partially visible crops.
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Having those edge cases discussed early would give our team a clearer baseline before scaling annotation.
Clear communication throughout
Our labeling requirements evolved as we reviewed the imagery. The feedback process felt straightforward.
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A shared set of examples and regular checkpoints would help keep the dataset consistent across batches.
A practical approach to field data
Field images are rarely perfect. We appreciated the attention to shadows, occlusion and changes in lighting.
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These are the details we would want a labeling partner to consider when preparing data for crop-monitoring models.
Retail in practice
Annotation applications across retail workflows.
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Product recognition
THE CHALLENGE
Products have similar packaging and change between catalog versions.
THE SOLUTION
Annotate product instances and map visible attributes to the current catalog.
DATA DELIVERABLE
Reviewed bounding boxes data with a consistent label schema and documented edge cases.
Product recognition
+15%
Throughput target
A project target for improving throughput with task-specific training data.
Sub-CM
Resolution target
Where calibrated source imagery supports fine spatial measurements.
40%
Efficiency target
A project target for reducing review effort through assisted labeling.
Deployment options
Build your workflow around the infrastructure your team uses.
On-Premises
Secure local processing for sensitive datasets.
Cloud
Highly scalable infrastructure powered by AWS/GCP.
Edge
Real-time inference optimized for on-device hardware.
Formats & platforms
ONNX
TensorRT
Core ML
OpenVINO
LiteRT
TorchScript
PaddlePaddle
IMX500
Qualcomm
Huawei
Available formats depend on your model, runtime and target hardware.
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Live Infrastructure Telemetry
1.2BData Points
189Models
24Pipelines
3Alerts
TypeOriginOwnerStatus
Vision ModelGitHubDave DigsterHealthy
DatasetAWS S3Emily ThompsonSyncing
DeploymentGCPRobert BrownActive
Zero-Trust Dataset Confidentiality
Your requirements and proprietary sample assets are encrypted in transit and never used for public model training.