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You know this is wrong.

The AI tools you use answer without reading what you gave them.

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Most notetakers don’t read all your transcripts.

You ask for insights from a whole folder of transcripts. They read part of it and answer anyway.

Screenshot of a notetaker's reasoning. 'read all transcripts' is marked in blue; 'Reading all 114 transcripts individually would be impractical' is marked in red Tap to enlarge.

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They read the notes instead.

A note is already a summary of the transcript. What you get back is a summary of a summary.

Same screenshot. Marked in red: the word 'notes' in each of the six 'Looked deeper into … notes' lines, and 'I've now gone through summaries for all 114 meetings' Tap to enlarge.

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And they decide what gets read.

You wrote “read all transcripts”. They pick a sample, which transcripts and how many to read, and you have no say.

Same screenshot. Marked in red: 'a targeted subset' and 'the highest-signal ones' Tap to enlarge.

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Most summarisation tools do the same.

You ask a question about all your data. They scan fragments of it and answer.

Screenshot of a summarisation tool's answer to 'are you sure you read all the raw data?'. Marked in red: 'no, I have not read every single word across all 103 raw transcript files', 'When you ask a broad question', 'the system retrieves representative passages', 'rather than reading hundreds of thousands of words in one continuous pass', 'result of algorithmic sampling' Tap to enlarge.

The data you gave is right.The insights you get back aren’t.

Your transcripts are the most honest data your company owns.

What you gave: all 138 transcripts, from the past quarter.
What most notetakers read: a sample of summaries you have no say on.

Transcripts from

Insights for

Inaccurate insights cost you revenue.

If your team is sitting on transcripts and accurate qualitative insights are important to you, we’d like to talk.

Write to davide@hxai.co.uk to book a call.