I was reading MeetGeek's latest blog post this morning, the one titled "The Science Behind Our AI: Achieving 95% Accuracy in Meeting Transcription," and I have to say, it stopped me mid-sip of my coffee. That 95% claim is a really bold number, and it doesn't match what I'm seeing in my actual usage logs at all.
For context, I've been running MeetGeek alongside a legacy transcription service for about four months now across my marketing team's internal syncs, client kickoff calls, and some external webinars. My process is pretty manual, but I believe it's fair:
* I take the same recorded meeting video file.
* I run it through MeetGeek.
* I run it through the other service (which is known for high accuracy but slower turnaround).
* I then do a manual, line-by-line comparison of the two transcripts against the original audio, marking any discrepancies in proper nouns, technical marketing terms (think "CTR," "MQL," "attribution modeling"), and complex sentences.
My aggregated accuracy rate for MeetGeek, calculated from the last 50 meetings, sits consistently around 82-84%. The other service averages about 92-94%. The 95% claim would suggest my experience is a significant outlier, which seems unlikely given my sample size.
The discrepancies I see most often are:
* **Industry Acronyms & Tool Names:** "Salesforce" occasionally becomes "sales force," "HubSpot" is sometimes two words, and "GA4" might be transcribed as "G.A. four."
* **Technical Jargon:** Words like "idempotent" (came up in a data pipeline discussion) or "funnel velocity" often get garbled.
* **Speaker Differentiation in Noisy Settings:** When multiple people talk quickly on a video call with less-than-perfect mics, the speaker labels can get swapped.
I'm a huge proponent of MeetGeek's workflow integration—the automatic summaries and CRM sync are fantastic—but this accuracy gap is material for my use case. I rely on the transcript text for feeding into our content repurposing pipelines and for generating detailed client meeting reports.
My question to the community—and hopefully to the MeetGeek team if they're listening—is: how are they defining and measuring that "95% accuracy"? Is it on a specific, cleaner dataset? Are they using a Word Error Rate (WER) calculation that might discount some of the errors I'm catching? Has anyone else done a similar side-by-side comparison and gotten results closer to their claimed number? I'd love to see their methodology, because right now, the marketing claim and the operational reality in my Martech stack are not aligning.
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