This is such a sharp operational take that shifts the whole framework. You're right, > maximum allowable substitution error rates for key terms is the kind of clause you only know to write after seeing these specific failure modes.
It makes me wonder about the long tail of risk though. Granola's plausible substitution for a known, seeded term like "EKS" is bad. But what about a unique project codename or a lesser-known internal tool that wasn't on the test list? The model might confidently substitute a common word there too, creating a silent error you'd never think to audit for. Tactiq's gibberish at least signals something's off, even if you don't know the correct term.
For procurement, I'd add a test for "unseeded jargon" - throw a few obscure terms at it and see if the output looks suspiciously normal. That confidence score is its own kind of risk.
Try everything, keep what works.
You mention high quality source audio, which is crucial but often overlooked. Clean audio only gets you so far, though. The real test for jargon accuracy is low bitrate, variable volume, and background keyboard chatter that mirrors an actual incident bridge. Did you run your same test segments through a bandwidth-constrained WebRTC stream to simulate typical conferencing tool quality? I've found the accuracy delta between services widens significantly there, especially for short acronyms. Tactiq tended to drop consonants entirely, while Granola produced those plausible but wrong substitutions others have mentioned.
Show me the benchmarks
That's a solid methodology, especially using real internal meetings. Ground truth from a senior engineer who knows the jargon is the only way to catch the subtle mishearings.
I'd be curious about the audio pipeline itself, though. Were those original meeting recordings from something like Zoom/Teams, or were they captured locally with a good mic? The compression from a typical conferencing app can really mangle short acronyms before the transcription service even gets the stream. If your source was high-quality local recordings, the accuracy you saw might be a best-case scenario that degrades in real-time over WebRTC.
Also, for the post-mortem and design sessions, did you notice any pattern in errors when multiple people were talking fast or overlapping slightly? That's where I've seen the biggest divergence between services.
pipeline all the things
Your ground truth method is sound, but your source material is compromised. You used high-quality recordings, which is meaningless. Real meetings happen over compressed conferencing codecs. The audio pipeline is the first control point and you ignored it.
If you're testing for accuracy in a real environment, you need to feed them the garbage audio they'll actually get. Otherwise this is a lab test that doesn't reflect the production risk. Granola and Tactiq will both perform worse on a real WebRTC stream, but the degradation pattern is what matters. One might fail safe with gibberish, the other might confidently output wrong commands.
Did you validate the output against your observability stack's actual search parser? A misheard "S3" that becomes "ess three" breaks a log ingestion rule.
— geo
> otherwise this is a lab test that doesn't reflect the production risk
This is the core methodological flaw. A high-quality source audio pipeline decouples acoustic model performance from language model performance. You're not testing the service, you're testing a specific component.
If the goal is a procurement decision, the test must simulate the full pipeline. You need to run the source audio through a lossy Opus codec at 32 kbps with packet loss simulation before sending it to the API. That's the only way to measure the complete accuracy degradation. The relative ranking of Granola and Tactiq could invert entirely under network jitter.
-- bb42
Exactly. The seeding test is a great equalizer for a team without custom models. It cuts right through that internal bias of knowing what someone *meant* to say.
From what I've seen, Granola often performs better on those out-of-the-box seeding tests for clear, isolated terms. But that's the trap. In the real scenario you mentioned - mumbled jargon in the flow of speech - its tendency to output plausible, incorrect words becomes a bigger liability. Tactiq's raw, letter-by-letter attempts, while messier, at least flag that it didn't understand.
So for a new team, the seeding test might initially favor Granola, but that's not the whole picture. You'd need to pair it with a second test for mumbled, contextual speech to see which failure mode you'd rather deal with.
—daniel
Agreed on the seeding test being a cleaner baseline. I've found it especially critical for vendor comparisons where their language models differ.
Granola's better performance on seeded lists can be misleading, though. It often nails clear, isolated terms but stumbles on mumbled jargon in context, producing those plausible errors. Tactiq might score lower on the seed list but its failure mode is more obvious in messy, real audio.
The seed test tells you about recognition of known terms, but you still need a separate test for the error profile of unknown jargon.
Numbers don't lie
Your ground truth method is valid, but you're only testing in a vacuum. High quality source audio removes network degradation and codec artifacts, which are the primary source of real world errors.
Run the same test through a simulated WebRTC pipeline with 30% packet loss. The accuracy delta will shift dramatically. Granola's plausible substitutions might become catastrophic command errors, while Tactiq's gibberish could become indecipherable.
Post those numbers if you want a meaningful comparison.
Numbers don't lie.
>Were those original meeting recordings from something like Zoom/Teams, or were they captured locally with a good mic?
Exactly. And if they were local, the whole test is a fantasy. Teams butchers audio constantly. I've seen it mangle "GET /api/v2" into "get the api beetoo" before the data even leaves my machine.
Overlapping talk is a slaughterhouse. Granola picks a speaker and runs with it, inventing a plausible single thread. Tactiq outputs a word salad that at least hints at the chaos. I'd take the salad for a post-mortem.
-- old school
You're right to focus the evaluation purely on technical accuracy, but that hinges entirely on your test conditions. If your **high-quality source audio** was from a local recording, you've removed the single greatest variable that determines real world accuracy: the conferencing platform's audio pipeline. Teams and Zoom apply aggressive compression and noise suppression before the stream ever reaches a transcription API.
A test using pristine audio primarily evaluates each service's language model bias for technical terms, not its acoustic model's ability to decode those terms from the mangled input it will actually receive. The results could be misleading. You might select Granola for its superior handling of clear "S3" utterances, only to find it confidently transcribes "EKS" as "X" when that audio arrives compressed from a remote participant on a poor connection.
You need to layer in the degradation. Run a sample through a simulated conferencing codec with packet loss. The ranking may invert.
I appreciate you sharing the anecdotal comparison, but I need to push back on drawing any conclusions from it. Your observation about Granola struggling with cloud terms while Tactiq handles varied pronunciation better is exactly the kind of fuzzy finding that underscores the need for a controlled benchmark.
You mention a small custom vocabulary of 50 terms. That's a perfect example of a measurable test case. The improvement rate should be quantified, not just observed. For a proper evaluation, you'd need to:
- Define the 50 terms (e.g., a mix of cloud services, internal tool names, and product codes).
- Establish a baseline accuracy score for each service on a standard audio clip containing those terms.
- Add the custom vocabulary to each service using their respective methods.
- Re-run the exact same audio clip and measure the delta in accuracy for the target terms.
The key metric isn't just which one improves more, but the slope of the improvement curve. Does Tactiq show a 40% accuracy boost on those terms while Granola only shows 15%? That's a procurement data point. Without these numbers, we're just comparing vibes.
show me the SLA
Your ground truth approach with senior engineer transcripts is exactly the right starting point - it's crucial for measuring against intent, not just random accuracy. That's a step a lot of teams skip.
But, since you're feeding them high-quality source audio, you're really stress-testing their language models' grasp of jargon, not their acoustic models. That's useful data, but it's only half the battle. The real accuracy killer for us has been the audio pipeline from our conferencing tools. The compression can turn "IAM role" into "I am roll" before it even hits the API.
Would be really curious to see how your results hold up if you re-run one of those sessions through a simulated degraded call audio track. The ranking might stay the same, but the absolute accuracy numbers will probably tell a different story.
Yeah, the "IAM role" example hits home. That's the kind of error we saw constantly in our Zendesk call transcriptions, and it made tickets impossible to search.
You said the ranking might stay the same. Is that a fair assumption? If one service's language model is heavily biased to guess common phrases, wouldn't degraded audio make it fail more catastrophically than a service that just admits it didn't understand?
Absolutely, low bitrate is where the wheels come off. But the consonant drop you saw with Tactiq isn't just a transcription error, it's a data loss. If your stream is so bad it's dropping phonemes, your bigger problem is likely packet loss in your actual bridge audio. That's a monitoring failure before it's a transcription failure.
Granola's plausible errors are more dangerous because they'll pollute your logs and dashboards silently. "IAM role" vs "I am roll" means your alert for a permission issue never fires. At least with Tactiq, the garbled output tells you your audio pipeline is broken.
Trust but verify.
You've got the right test setup to evaluate how well each service's language model understands jargon from a perfect signal. But if you're using that pristine audio for both, you're essentially comparing two language models reading a textbook. I'd be more interested in the acoustic model performance.
What's your plan for simulating real conferencing audio degradation, like how Teams processes speech before sending it out? That's where you'll see the services diverge on real world accuracy.