When AI keeps getting names wrong in meeting summaries, the summary step is usually not the dumb part. The errors were planted earlier: names are misheard during transcription, statements are attached to the wrong person during speaker separation, and the summary then writes those errors into the minutes as fact. Troubleshoot along that chain, locate which step broke, and fix that step, instead of switching tools on impulse.
Step one, transcription: misheard, or substituted?
Take each wrong name in the minutes and search for it in the raw transcript. If the transcript is already wrong, the problem is transcription: names, rare characters and accented pronunciation are exactly where speech recognition is weakest, because they are rare in everyday training data and the model prefers a common homophone. A surname heard as a more common one, or a product name split into two ordinary words, are classic cases. If the transcript is right and the minutes changed it, the fault sits in step three. Three fixes help here: feed the attendee list, project names and key terms to the tool or into the prompt before the meeting; require the minutes to keep the transcript's spelling at first mention; and for important meetings, run one global replace of frequent error words over the transcript before summarizing.
Step two, speaker separation: who said it breaks more easily than what was said
Multi-person meetings hide a more dangerous error type: the words are perfect, but the speaker label is wrong, and one person's commitment is booked to another. This is the riskiest error in minutes because readers rarely notice it, while follow-ups and accountability then land on the wrong desk. It appears when voices are similar, people talk over each other, or a remote connection sounds poor. Fixes: record with one microphone per person where possible, or a meeting tool with separate tracks; spend one minute before the meeting binding speakers to names if the tool allows it; and after generation, always re-listen to the original audio for any commitment, number or deadline attribution before forwarding the minutes.
Step three, summarization: uncertainty gets written as certainty
Any noise from the first two steps gets amplified once more here. When writing minutes, a large model tends to complete vague references into specific names, because the genre demands prose that reads complete and smooth; it would rather guess than admit a passage was unclear. Set rules for this step: state in the prompt that names, numbers and dates that are uncertain must keep the transcript's wording or be marked for confirmation, never silently completed. Then proofread in reverse: check every name in the minutes against the transcript's name list, and chase down each extra or mismatched one.
A post-meeting checking order you can use directly
- People first: is every name in the minutes on the attendee list, spelled consistently?
- Attribution next: for sentences carrying commitments, objections or ownership, is the speaker right? When unsure, re-listen to that segment.
- Numbers after that: amounts, dates and ratios, checked one by one against the transcript or audio.
- Absences last: someone who never spoke appearing in the minutes, or a heavy speaker vanishing entirely, both signal a separation failure.
Run in this order, an hour-long meeting usually takes five minutes to verify. In the end, wrong names are not one vendor's patent; they are errors that transcription, separation and summarization each introduce along the chain. A fixed checking routine beats a tool swap.
