Enterprise AI is increasingly moving beyond clean, structured datasets and into the much larger world of documents, files, images, forms, emails, reports, contracts, claims, clinical records, and other unstructured content. These files often contain the information enterprises need most, but they are also among the hardest data assets to process reliably at scale.
A document AI production system is much more than an LLM, a vector database, or an RAG application. Before a document can become useful AI context, it may need to be securely uploaded, validated, scanned, classified, parsed, OCR-processed, enriched with metadata, protected from sensitive-data exposure, and transformed into retrievable knowledge. The resulting content must then remain traceable to the source through retrieval, reasoning, human review, and downstream execution.
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