Harvey said it rebuilt Review Table loading to page rows from the backend, improving responsiveness for larger legal document reviews.

Harvey said it redesigned Review Table loading so large legal document reviews open faster and remain responsive as row counts and column counts grow. The engineering update matters because the company described Review Table as a collaborative system for analyzing thousands of documents, with each row tied to a file and each cell backed by model reasoning and citations.

In a blog post published on 22 October 2026, Harvey said Review Table is its largest and most exhaustive inference engine, used to standardize answers in a structured table that lawyers can sort, filter, verify, lock and comment on. Harvey said the feature is intended to become a reusable source of truth for later memos or presentations, but the browser-based loading approach began to break down as teams queried hundreds of columns across thousands of files.

Harvey said the old design performed all table operations on the client side, loading the entire Review Table into the browser and handling sorting, filtering and aggregation in memory. That approach worked for smaller tables, but the company said tables could lag, become unresponsive or fail to load entirely once they exceeded thousands of rows.

To address that limit, Harvey said it moved client-side operations to the server side and adopted a CQRS-style split between Postgres, which remains the source of truth, and Turbopuffer, which provides the read interface. Harvey said Turbopuffer fit the system because it runs on top of Azure blobs and supports vector search, full-text search and filtering, but it also required new abstractions because it does not support pagination out of the box.

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