1/ Okay, so Google’s AI Extract is basically Document AI but on steroids. It OCRs your PDFs/forms, auto-identifies key−value pairs, tables, layouts and turns chaos into neat structured data that even your analytics pipeline can swallow. Native GCP integration? Big yes.

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2/ It’s built around processors: Form Parser for generic forms, Layout Parser for tables/text chunks, and Custom Extractor where you define your own schema can be foundation–model based (just a few labels) or full-on trained. Super flexible.

3/ Then there's Gemini. Use it to extract structured JSON from PDFs or even chunk + reason about docs at scale. Multimodal prompts = OCR + smarts. Gemini 2.0 + Genkit show how you can treat PDFs like data sources, not just blobs.

4/ Best part? It’s not just dump-file-and-forget. Workbench lets you auto-label, train, tweak. You can pipeline everything—Cloud Storage → Document AI → BigQuery → Vertex AI. All the cloud tools playing nice together.

Sources:

https://cloud.google.com/document-ai/docs/overview

https://ai.google.dev/gemini-api/tutorials/extract_structured_data

https://cloud.google.com/blog/products/ai-machine-learning/use-gemini-2-0-to-speed-up-data-processing

5/ Companies are using this for invoices, medical forms, contracts, even ID verification. The Gemini structured-output API means you can get clean JSON right away. We're not far from “AI Extract” being everyone’s secret data weapon. 🚀