It's been effective for extracting structured data from KYC documents, like driver IDs and operating cards, without needing to build and train a custom OCR model from scratch. The ability to train a custom vision model on our specific document types, rather than relying on a generic OCR engine, has noticeably improved accuracy for Arabic-language documents and region-specific formats we deal with. Integration with the rest of our Google Cloud stack was smooth, and the interface for labeling training data and managing models is accessible enough that our team could iterate without needing deep ML expertise. Review collected by and hosted on G2.com.

Training a reliable custom model takes a fair amount of labeled data and iteration upfront, which required real time investment before we saw accuracy levels good enough for production use. Pricing for both training and ongoing predictions can get expensive at scale, especially compared to simpler OCR alternatives, so cost planning needs to factor in both phases. Some document layouts with heavy variation, like older or damaged IDs, still occasionally produce extraction errors that require manual review, so it hasn't fully eliminated the need for human verification in edge cases. Review collected by and hosted on G2.com.