Meeting Proofreader

Review the names, numbers, and decisions most likely wrong in multilingual meeting notes

Problem

After a multilingual meeting, transcription is fast but proofreading takes almost as long as the meeting itself: speaker labels get mixed up, and names, amounts, decisions, and owners are the most error-prone parts, exactly what the notes are for. Current tools hand you a wall of text and leave the reading to you.

Solution

Import a transcript or local recording, automatically extract people, numbers, dates, decisions, and owners, and rank them into a review list by downstream impact, jumping straight to the matching audio segment to fix as you listen. Confirmed versions export as a structured summary.

Users

Project managers in cross-border teams, remote assistants, ops and consultants who clean up multilingual meeting notes

MVP

Transcript import with speaker segmentation; key entity extraction and risk ranking; click-to-jump audio segments; item-by-item confirm or correct; structured summary export

Revenue

Subscription by processing hours or meeting count; team tier adds shared glossaries and speaker voiceprint library

Why now

Speech-to-text is cheap and accurate enough that the bottleneck has moved to proofreading, which almost nobody has productized specifically