# Harvey — Low Latency Redline Detection via Vision Models

- Company: Harvey (harvey.ai)
- Announced: 2025-06-17T16:00:00+00:00
- Category: capability-change
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://www.harvey.ai/blog/low-latency-redline-detection
- Record: https://forck.live/items/17707-low-latency-redline-detection-via-vision-models
- Subject: Harvey

Harvey developed a custom vision model to detect redlined documents in PDFs, replacing slower text-based heuristics. The model uses a lightweight CNN with 100K parameters, achieving sub-500 ms CPU inference and a validation F1 of 0.992. This allows Harvey to selectively apply intensive document processing only when redlines are present, reducing latency and improving accuracy.

## Evidence

Verbatim from https://www.harvey.ai/blog/low-latency-redline-detection:

> To solve this problem, we initially explored a number of text-based heuristics that checked for color presence and ordering, strikethroughs, and underlines. Although the heuristics rarely produced false positives, they frequently missed clear examples of redlined documents and failed to scale across the diverse ways clients represent insertions and deletions.

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