Granola vs Tactiq for real-time transcription accuracy

Good point. Comparing their ability to guess from perfect audio only tells us about their internal dictionaries, not their real-world resilience.

For simulating degradation, I've seen folks get decent results by running clean audio through a low-bitrate Opus encoder, maybe with some simulated packet loss. That mimics the codec path. But you're right that replicating Teams' specific noise suppression is trickier. I wonder if the real test is just feeding them actual archived Teams recordings and comparing.

I like your approach, especially the use of a senior engineer for the ground truth transcript. That's key for judging intent, not just raw word accuracy.

Your mention of high-quality source audio is interesting though. It means you're mainly stress-testing their language models' internal knowledge of technical terms, which is useful, but it sort of skips the most common failure mode in a real workflow. Most of us are dealing with the audio after Teams or Zoom has had its way with it.

I'd be really curious if the gap between Granola and Tactiq widens or narrows when you feed them a version of that same audio after it's been run through a low-bitrate Opus encode to simulate a typical call stream.

Your methodology's use of high-quality source audio is a controlled starting point, but it fundamentally tests language model bias, not the acoustic model's resilience to degraded input. The absolute accuracy numbers you get from this clean feed will be misleadingly high and likely compress the practical difference between the services.

When you process that same audio through a low-bitrate Opus codec to simulate a real conferencing pipeline, you'll likely see a divergence. A service with a stronger acoustic model will maintain a higher proportion of its clean-audio accuracy, while one reliant on pristine input for jargon recognition will degrade more sharply. The error profile will also shift from plausible substitutions to garbled phonemes, which changes the operational risk.

The real test isn't which service understands "EC2" from a perfect recording, but which one can disambiguate "EKS" from "X" when the high-frequency consonant data is lost to compression. Your current setup answers the first question, not the second.

Solid methodology. That ground truth from a senior engineer is key, especially for judging if a term substitution changed the *meaning* of a discussion.

But, feeding them high-quality source audio means you're primarily benchmarking their language models' internal dictionaries, not their acoustic resilience. That's fine for a best-case scenario. The real ranking will likely shift when you run that same audio through a low-bitrate Opus encode to simulate a real conferencing stream. A service that's good at guessing "EKS" from perfect audio might completely fall apart when the consonants get smeared by compression.

Exactly. The shift from plausible errors to garbled nonsense is a crucial point. A service that confidently spits out "I am roll" from degraded audio is way worse than one that just outputs a bunch of jumbled letters, because the wrong guess makes it into your knowledge base silently.

That's the real operational risk you're buying against.

Totally agree on the silent corruption being the worst outcome. It's not just about knowledge base pollution, either. If that transcript feeds an alerting rule or an automated ticket classification, you're now actively making wrong decisions based on the error.

I wonder if the confidence score for each segment matters here. A garbled mess with low confidence could be flagged for review, while a high-confidence "I am roll" slips through.

Confidence scores are a bandage, not a fix. If your pipeline trusts them to gate automated actions, you're just moving the failure point.

I've seen an ingestion job drop a whole day of call logs because the vendor's confidence API started returning 0.95 for everything after a silent model update. No threshold will save you from that. The score is part of the data payload, and like any other field, it can be wrong or change without notice.

You need to monitor the distribution of the scores themselves. If they stop varying, your filter is broken.

That's a really practical point about monitoring the score distribution itself, not just using a static threshold. It makes sense that if all scores suddenly cluster, the metric has lost its signal.

In our setup, we feed transcripts into a CRM tagging system. Your example makes me wonder, has anyone found a good way to set up an alert for that kind of distribution shift? A simple variance check, or something more sophisticated?

Interesting approach using the senior engineer for the ground truth. That's smart for catching intent.

But, I'm a bit stuck on the high-quality source audio part. In our daily standups, half the team is on a subpar connection. The audio gets chewed up before it even hits the service. Wouldn't testing with clean audio mostly show which one has a better pre-loaded dictionary for our jargon, and not how well they handle the real, messy input?

Maybe you ran the degraded audio test too? I'd be really curious if the ranking changed when you simulated a bad call stream.

Clean audio for a benchmark is fine, but you're paying for the real-time product. Their real-time engine is different from the batch processing API, often with stricter latency budgets and lighter models.

Did you compare the live transcription accuracy during a simulated meeting to the post-processed results from the same audio file? I've seen vendors where the gap is over 5% WER.

The "cost and integration are secondary if the words on the screen are incorrect" premise is the kind of line you only get to write before you've actually been through a real procurement cycle. In the real world, accuracy is a threshold, not a ranking metric. Once you're past "good enough to be useful," which both of these services likely achieve, the decision flips entirely to operational cost and how many headaches the integration creates.

I've watched teams reject a 2% more accurate option because its API rate limits would have required a complete re-architecture of their ingestion pipeline. The silent errors you're worried about are often dwarfed by the loud, daily errors of a service that can't scale or plays poorly with your other tooling.

That's a good reality check. I guess I've been focusing too much on the accuracy numbers from reviews and not enough on the actual day-to-day fit.

How do you figure out where that "good enough" accuracy threshold is before you commit? Is it just trial and error with your own meeting audio, or are there better signals?

The "good enough" threshold is the point where manual review costs exceed the accuracy gains. Don't guess. Run a two-week pilot with your actual meeting audio and log errors that cause a follow-up question or a misunderstanding. Count them.

If you need a second person to fix transcripts more than once a day, it's not good enough. If errors only happen on the garbled audio from the remote guy with the bad connection, and everyone just knows to clarify his points, then maybe it is.

Piloting on your own audio is the right idea, but you're missing the trap in "log errors that cause a misunderstanding." Who decides what constitutes a misunderstanding? If you're not using a ground truth transcript to compare against, you're just logging *perceived* errors, which are biased by what the listener already knows about the meeting.

Two people can listen to the same flawed transcript and disagree on whether it caused a problem. Suddenly your error count is a subjective team survey, not an objective metric. You need the original script or a verified human transcript to measure against, otherwise you're just measuring annoyance.

>"cost and integration are secondary" is how you end up paying $8k a month for a 1% accuracy bump.

Did you even get a quote? The last time I looked, Granola charged per-second of audio, while Tactiq had a per-user seat license. The cost scaling for your "fully transcribed, searchable archive" is completely different. A high-accuracy service that bankrupts you is useless.