Delivered structured data annotation and quality assurance services to support AI-ready dataset preparation and machine learning workflows.
Project note
Client
Shaip
Scope
Data Operations
Status
3 months
The honest version
Shaip required structured datasets to support AI/ML workflows and needed an operations partner to handle annotation work with consistent quality control.
We built annotation workflows with multi-layer quality assurance checks so data processing could remain structured and reviewable.
Data annotation workflow delivered
QA process added across batches
Dataset preparation supported
Processing workflow improved
Scope map
Data labeling and annotation
Dataset categorization
Quality assurance checks
Data validation
Structured dataset preparation
Execution model
The point was not to create separate assets. It was to make brand, product, campaign, onboarding, and launch activity work from the same plan.
Workflow design
Data labeling
QA checks
Validation
Dataset delivery
Build sequence
01. Annotation workflow design and team briefing
02. High-volume data labeling and categorization
03. Multi-layer quality assurance and validation
04. Structured dataset packaging for AI/ML pipelines
Visual references
Images are used as visual context for the type of work and should not be read as performance claims.
Current outcome
Supported Shaip's AI/ML data operations with structured annotation workflows and quality assurance checks.
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