How AI Meeting Summaries Work (And Where They Get It Wrong)

If you have used an AI note-taker for more than a few weeks, you have probably noticed two things: the summary is sometimes startlingly good, and sometimes confidently wrong. Both come from the same underlying pipeline. This post explains how that pipeline works in 2026, where it fails, and what to do about each failure mode.

The pipeline in four stages

Almost every commercial AI note-taker — Mavio included — does the same four things, in roughly the same order:

1. Capture audio from the call. Either a bot joins as a participant, or a browser extension grabs the audio from the meeting tab. Output: a continuous audio stream.
2. Speech-to-text (transcription). The audio is fed through an automatic speech recognition (ASR) model — Whisper, Deepgram, or a vendor-trained variant — which produces a time-stamped transcript with speaker turns.
3. Speaker diarization. The transcript is split into utterances and each utterance is labelled with a speaker tag. Names come from meeting metadata when available, or get inferred from speaker introductions.
4. Summarization. The transcript is chunked and fed into a large language model (Claude, GPT, or a fine-tune) with a prompt asking for an executive summary, key decisions, and action items.

The output you see — a five-paragraph summary, a list of decisions, a checklist of tasks — is the LLM's interpretation of the transcript, not the conversation itself. That distinction matters.

Where each stage gets it wrong

Capture — overlapping speech

Two people talking simultaneously is the single most common audio failure. Even a strong ASR model produces garbled output when more than one voice is active. Some tools partly solve this by recording each participant's microphone stream separately when it's available (Zoom recorded sessions, Google Meet pro), but tab-capture and bot-mix audio cannot easily separate two speakers in the same audio frame.

Practical fix: nudge meetings toward one-speaker-at-a-time hygiene for sections you actually want captured cleanly — decisions and action items.

ASR — accents and jargon

ASR models do best on the accents and vocabulary they trained on. For most major models this skews toward North-American English, neutral pacing, and tech-industry vocabulary. Indian English, Singaporean English, and heavy regional accents see materially worse word-error-rate. Industry-specific jargon (legal, medical, pharma) routinely transcribes wrong unless the vendor has a domain-specific model.

Practical fix: spell out important proper nouns and product names early in the call. They will be transcribed correctly thereafter.

Diarization — short utterances

Speaker labelling works well when each person speaks for at least a few seconds at a time. It fails on rapid back-and-forth ('yeah'/'right'/'mm-hm') and on calls with similar voices. You will see the transcript flip speakers for a short interjection and then revert.

Summarization — hallucination and recency bias

This is the headline failure mode. The LLM does not transcribe; it generates plausible text given the transcript. Two common errors:

• Hallucinated commitments. A speaker says 'I think we could explore that next quarter' and the summary lists it as a Q3 deliverable. The LLM has paraphrased a hypothetical into a commitment.
• Recency bias. On long meetings, the summary skews toward the last 10–15 minutes of conversation because the model sees less compression pressure at the end of the transcript window.

Practical fix: never ship the AI summary to a customer unedited. Read it. The summary is a fast first draft, not a record.

How Mavio handles each

We have no magic. We make the same trade-offs as everyone else, with a few specific choices:

• We use a speaker-aware ASR pipeline that uses the meeting metadata (participant names) when available, with fallback inference for ad-hoc joiners.
• Our summarization prompt is explicitly constrained to distinguish 'decided' from 'discussed' — we measured a 40% drop in hallucinated commitments after that change.
• On meetings over 30 minutes we use a hierarchical summarization pass: each 5-minute chunk is summarized first, then the chunks are summarized into the final note. This reduces recency bias on long calls.
• Every Mavio meeting note links back to the source utterance so you can verify any claim against the actual transcript in one click.

What to ask any vendor

Three questions cut through the marketing copy:

1. What's your word-error rate on Indian English speakers? (Or whatever your team's dominant accent is.) If the answer is a number, ask how it was measured.
2. How often does the summary cite a commitment that was actually a hypothetical? (Vendors that have measured this will give you a percentage.)
3. If I want to verify a single line of the summary, how many clicks to the underlying transcript line?

Try Mavio

If you want to see the four stages above in action — including the source-utterance link on every summary line — try Mavio free. The free plan covers five meetings per month.