Data Annotation and Labelling – How HITL Enhances Accuracy in AI Model Development
How Many Data Annotators Do You Actually Need? A Benchmark of 20M+ Monthly Annotations Across LiDAR, Video & Geospatial Projects
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30%
Saving
About the Customer
The client builds data products using publicly available data on the Web. They provide businesses with the actionable insights in two solution areas – Retail Intelligence & Brand Analytics, that resides outside the firewalls (in the public domain) through aggregation, curation and analysis of this data.
Problem
The company required assistance in comparing entities across competitors on specific features to evaluate match type and also to adopt the platform to new industries and domains. With the growing demand and customizations, they needed to provide data and insights to development team with reasons for incorrect suggestions/matches by the algorithm.
Approach
The quality of data fed to any machine learning system is an ever growing concern for any business. Validating and Verifying the data is done through a tedious manual and automated process. This also helped the client verify the data fed into the AI Algorithm. The data insights derived during the data validation and data verification process enabled the client to develop customized products for different industries and domains.
Results
The client partnered with NextWealth to help them validate and verify the data. NextWealth helped them arrive at smarter pricing and promotions resulting in increase in profitability. Data Insights provided by us also led to faster time to market for the BI platform by enabling it to adapt quickly to new sectors(Hotels to E-commerce) and competitors within sectors. The insights also led to improved algorithm accuracy for a given use case through insights and tight knowledge sharing with development team.
Services Provided
Data Verification
Data Validation
Data Insights
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Data Annotation and Labelling – How HITL Enhances Accuracy in AI Model Development
Data Annotation Accuracy Benchmarks 2026: 95–100% Quality Rates Across Computer Vision, Trust & Safety, and Generative AI
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