We're evaluating Fireflies.ai for post-incident meeting transcription. The accuracy on general conversation is acceptable, but it consistently mangles critical SRE and infrastructure acronyms.
Examples from last week's bridge:
* It transcribed "MTTR" as "matter" or "empty are".
* "P99 latency" became "p ninety nine latency" (okay) but also once as "pine tree ninety latency".
* "SEV-2" was written as "seven two".
This creates extra work for postmortem documentation and risks miscommunication. The tool seems to lack a configurable glossary or the ability to learn from corrections.
Has anyone built a reliable workaround? Or is this a fundamental limitation of their NLP model for technical fields?
—D
Five nines? Prove it.
That "pine tree ninety latency" one is actually kind of funny, though I know it's frustrating when you're trying to document an incident. I ran into the same thing when we were trialing it for sales calls, but with product acronyms instead.
I'm curious, did you try speaking the acronyms as separate letters? Like saying "M T T R" clearly instead of "matter"? I found that helped a bit with ours, but it's not a great solution because you have to remember to talk unnaturally. It really seems like a major oversight for them not to have a custom glossary, even as a paid feature.
That's a solid workaround, and I do the same thing with client calls when I'm testing a new transcription service. But it's a band-aid that doesn't scale, especially when you're in a heated discussion.
The real issue, as you mentioned, is the lack of a configurable glossary. For a tool aimed at business meetings, it's a surprising omission. Most decent conversational AI platforms have at least some ability to weight or add specific terms. Fireflies seems to be using a very general model and calling it a day.
I've had to implement post-processing scripts as an interim fix, which adds another step and potential point of failure. Have you found any other service that handles this glossary problem better in your sales calls?
Integrate or die
This is a classic limitation of generalized transcription models when they encounter domain-specific jargon. The issue isn't just the lack of a configurable glossary, it's that the acoustic model is trained on common speech patterns. "M T T R" spoken quickly truly does sound like "matter" to an algorithm that hasn't been fine-tuned on DevOps vocabulary.
I've found the workaround of spelling out acronyms to be unsustainable during actual incident response, where communication speed is critical. My approach has been to run the raw transcription through a simple post-processor using a regex dictionary for known terms. It's a clunky, two-step process, but it corrects the bulk of the errors.
For a tool marketed for business meetings, this feels like a fundamental architectural oversight. They should at minimum offer a per-workspace custom vocabulary file, similar to how speech recognition engines have worked for decades. Have you considered piping the output into a local script before it hits your documentation?
This is a classic problem in speech-to-text with narrow-domain corpora. Your "pine tree ninety latency" example is particularly telling: it's not just about acronyms, but the acoustic similarity of "P" and "T" when spoken quickly, compounded by the model's lack of context.
Most general-purpose models, like the one Fireflies likely uses, are optimized for common speech and broad business terms. They fail on SRE vocabulary because the training data contains almost no instances of "P99" or "SEV-2" as distinct tokens. The model treats them as novel phoneme sequences and maps them to the closest known words, hence "matter" for "MTTR." It's a data problem, not just a missing feature.
Have you measured the error rate on a sample of your meetings? Quantifying the inaccuracy could help build a case for either a custom glossary request or justify moving to a service that allows fine-tuning. For critical postmortems, even a 5% error rate on key terms introduces unacceptable risk.
numbers don't lie