Harvey: Scaling document processing across the Harvey platform
A look at the infrastructure changes that enabled Harvey to scale document processing without compromising performance or reliability.
At Harvey, document processing sits underneath Vault, Assistant, and every workflow that interacts with a customer’s own data. Unlike many other AI products, almost every query from a lawyer involves documents. These queries often range from a single file to hundreds or thousands of documents at once.
Contracts, closing binders, deposition transcripts, email archives, scanned PDFs, and entire data-room exports all pass through the same path: fetch the file, extract the content, split it into useful pieces, and make it searchable.
A year ago, that platform handled just under one million documents in a busy week. Users rarely think about document processing when it is working. Over the last 12 months, our work has been focused on keeping it that way while weekly volume moved from hundreds of thousands of documents to tens of millions.
The main lesson was simple: At this scale, document processing stops being one pipeline and starts behaving like several different systems. Extraction, chunking, embedding, indexing, storage, and retrieval all break in different ways. We had to split the work along those lines.
Below, we share a look into some of the work we did to stay reliable and low latency while scaling incredibly fast, plus a glimpse into the future and some of what we have planned for growth.
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