Your OCR Engine Is Either Your Biggest Asset or Your Biggest Risk.
If you're processing 150k to 5 million documents a year, a 1% OCR error rate isn't a rounding problem. It's a manual review queue that never shrinks.
Generic OCR models were trained on clean, digital-native PDFs. Your documents aren't clean. They're folded, glared, photographed at an angle, and uploaded on a 2G connection from rural India.
We ran the numbers with our customers. The data proves that stability isn't accidental. It's engineered.
Get the reportWhat’s inside?
Industry Benchmarks
Understand benchmark performance across key Indian ID documents, including typical accuracy, automation, and throughput observed in large-scale identity verification deployments.
Technical Standards
Compare how different OCR and identity verification providers perform across core ID fields, and the operational impact on latency, exceptions, and manual review rates.
Edge Cases We Excel At
Examine common KYC edge cases—from fuzzy name matching and poor image quality to document format variations—that affect your bottomline.
"Those error rates literally went to half of what it used to be. Since the error rate went down, more applications would get processed — straight away passed on to an approved application."
Vishal Sharma
SVP Operations
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