Data Annotation and Labelling – How HITL Enhances Accuracy in AI Model Development
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97%
Accuracy Achieved
1.69M+
Data Points Processed
33
Specialised Workflows
About The Client
NextWealth enabled a global e-commerce marketplace to scale catalog processing across 1.4 million+ product comparisons. By combining Human-in-the-Loop (HITL) workflows, defined SOPs, and multi-stage quality controls, NextWealth delivered 100% quality compliance and 24-hour turnaround times—eliminating duplicate listings and enhancing search relevance.
Challenge/Problem
Catalog complexity & fragmentation
Rapid marketplace expansion across international regions resulted in millions of product listings with duplicate entries, inconsistent specifications, and fragmented attribute structures, making catalog management highly complex.
High-precision relationship accuracy
Complex product variations challenged automated AI classification systems, requiring reliable human validation and domain expertise to accurately identify Exact Match, Variant Match, Mismatch, and Unsure Match relationships.
Scaling quality-driven operations
Maintaining catalog accuracy while processing large volumes of data required a highly efficient execution model that could scale rapidly without impacting search precision or catalog integrity.
The Solution
Human-in-the-loop validation framework
A dedicated HITL delivery model was implemented on the MTurk platform, combining AI-assisted workflows with expert human validation to ensure accurate product relationship mapping at scale.
Structured product matching process
NextWealth analysts followed detailed SOP-driven workflows to review marketplace listings, validate product attributes, and establish accurate relationships across complex catalog records.
Multi-level classification accuracy
Products were systematically categorized into Exact Match, Variant Match, Mismatch, or Unsure Match classifications to create a consistent and reliable catalog hierarchy.
Quality governance at scale
Continuous calibration, multi-stage QC/QA checks, and First Time Right (FTR) controls were implemented to maintain process consistency, accuracy, and output quality across large-scale operations.
Key Results & Business Impact
100% quality accuracy
Maintained consistent 100% quality metrics across high-volume catalog batches through structured validation workflows and rigorous quality controls.
24-hour turnaround execution
Delivered rapid SLA execution to support continuous catalog updates, ensuring marketplace listings remained fresh and accurate.
Improved search relevance
Reduced duplicate listings and improved catalog organization, enabling better product discoverability and enhancing the overall buyer experience.
Reduced operational rework
Multi-level quality checks minimized errors and reduced manual intervention, lowering internal engineering effort and improving operational efficiency.
Services Provided
Let Us Help You Scale AI Catalog Quality Across Millions of Listings
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Data Annotation and Labelling – How HITL Enhances Accuracy in AI Model Development
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