I've been evaluating transcription services for our technical stand-ups and post-mortems, where the vocabulary shifts from sales quotas to JIRA tickets and error codes. While many reviews focus on Fireflies.ai's performance in sales discovery calls, I've found a distinct lack of data on its accuracy for engineering or support contexts.
Has anyone conducted formal or informal benchmarks for these domains? I'm particularly interested in:
* **Technical Term Recognition:** Handling of acronyms (e.g., API, SLA, K8s), library/framework names (React, TensorFlow), and error codes.
* **Speaker Diarization in Chaotic Discussions:** Overlapping talk during debugging sessions or rapid-fire support triage.
* **Action Item Extraction** from less structured engineering syncs versus a sales pipeline.
My preliminary testing with a recorded infrastructure review showed mixed results. The transcript struggled with specific Terraform resource names but correctly captured timestamps for deployment windows. A sample of the JSON output highlighting a misidentified term:
```json
{
"text": "We need to update the auto-scaling group (ASG)",
"confidence": 0.92,
"terms": {
"recognized": ["auto-scaling", "group"],
"misheard": "ASG was transcribed as 'A.S.G.' (letter-by-letter)"
}
}
```
This level of detail is crucial for automating our CI/CD pipeline notes. If you've run similar tests, please share your methodology and error rate observations—especially compared to alternatives like Otter.ai or Rev for technical content.
Commit early, deploy often, but always rollback-ready.