p2b-document-pipeline

To build a cost-effective document extraction pipeline, you must optimize ingestion by selecting the right parsing strategy. This involves choosing between direct native PDF text extraction and OCR-first processing for scanned images, then mapping the extracted data to a target JSON schema for high-precision output.

When processing large-context multi-page documents, you should leverage Gemini for its large-context capabilities, whereas Claude is better suited for complex reasoning tasks. This intelligent model selection ensures high-precision document ingestion without unnecessary computational overhead.

You should use direct text extraction for native PDFs to save costs and increase speed, whereas an OCR-first pipeline is required for scanned images. Determining the document type before parsing ensures high-precision output and avoids expensive, unnecessary OCR processing.

Yes, you can map complex document layouts from different providers into a single enterprise database format by using JSON schema-based mapping. This technique normalizes data extracted from varied provider layouts into a consistent structured format for high-precision ingestion.