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302 Hours of Hindi Audio Transformed into High-Accuracy, Searchable Data, Delivered under 3 Weeks

How Crystal Hues optimized transcription revision and NER tagging under tight timelines and a mid-project budget cut using the PECAT AI data annotation platform.

Audio Data Annotation

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The Highlights

End-to-end workflow: Transcription Revision → NER Annotation.
302 hours of Hindi audio processed with high linguistic accuracy.
Completed on the client’s PECAT multilingual AI annotation platform.
Seamless collaboration across multiple linguists for consistent output.
On-time delivery despite budget adjustments mid-project.
Enhanced data accuracy, enabling improved downstream AI performance.

What You’ll Learn

How we managed 302 hours of large-scale audio data annotation under compressed timelines.
How strategic resource planning enabled 100% on-time delivery despite a mid-project budget reduction.
How we ensured high-quality transcription with linguistic and contextual precision at scale.
How we implemented structured NER tagging to enhance data classification and improve downstream AI accuracy.
How optimized workflows allowed us to maintain quality while adapting to changing project constraints.
How the PECAT multilingual AI data annotation platform streamlined collaboration, consistency, and error tracking across multiple linguists.
How workflow optimization directly contributed to improved data accuracy for the client’s AI applications.

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