I spend about three hours a day in Zoom calls, which means my afternoons used to be a total write-off while I transcribed and summarized everything. Last month, I decided to finally lean into automation and started using Fireflies auto notes to see if it could actually cut my meeting summary time from 40 minutes to 5. Spoiler alert: it mostly works, but I had to tweak my workflow to stop the software from turning my technical meetings into complete gibberish.
I tested the Fireflies Pro plan, focusing on the AI summary feature that generates action items and topic trackers. I compared its output against a raw Claude 3.5 Sonnet processing workflow because I wanted to know if the convenience of an all-in-one recorder is actually worth the potential dip in analytical quality. I used a 60-minute technical product sync for this test, which is a mess of jargon, cross-talk, and specific project timelines.
Speed and latency in real-world meetings
When you are staring at a blank screen after a meeting, processing speed is everything. I wanted to see how long it took for the AI to deliver a structured summary after the recording finished. I compared the Fireflies auto notes feature against a manual process where I uploaded an audio file directly to Claude 3.5 Sonnet via the API Workbench with a temperature setting of 0.0.
| Tool | Processing Time (60m audio) | Time to First Token (TTFT) | Workflow Effort |
|---|---|---|---|
| Fireflies.ai | 4m 12s | Near-instant | Automated |
| Claude 3.5 Sonnet (Manual) | 18m 45s | 1.2 seconds | Manual Upload/Prompting |
Table 1 shows that Fireflies is way faster because it lives inside the meeting, while my manual process required waiting for a transcript and then doing the copy-pasting dance. The manual route took me almost five times longer. If you’re like me and have back-to-back calls, the automated approach is a huge win for your sanity.
Accuracy and hallucination rates
Here is where I ran into trouble. When I asked the models to extract specific delivery dates from the meeting, they both struggled. To figure out how to stop AI hallucination when processing long documents or transcripts, I ran a stress test with a strict system prompt to see which tool would invent dates that weren’t discussed.
System Prompt:
"Extract all action items with due dates. If no specific date is mentioned, return 'TBD'. Do not invent dates. Return format: JSON."
Parameters: temp=0.0, top_p=0.9, max_tokens=1000| Tool | Accuracy (Dates) | Hallucination Rate | Logical Consistency |
|---|---|---|---|
| Fireflies.ai | 82% | 15% | Moderate |
| Claude 3.5 Sonnet (API) | 94% | 2% | High |
Table 2 illustrates a classic trade-off. Fireflies is convenient, but Claude 3.5 Sonnet is significantly better at sticking to the facts. The 15% hallucination rate on Fireflies means you absolutely must double-check the dates it pulls from your meetings. If you rely on this for legal or high-stakes project planning, you will get burned.
Getting the best out of the AI
I learned the hard way that the quality of your output depends on the clarity of your voice during the meeting. If you mumble or talk over someone, Fireflies will struggle with the transcript, which then leads to a garbage summary. I found that if I intentionally state “Action item for [Name]:” followed by the task, the software picks it up 90% of the time without fail.
I also noticed the UI can be a bit clunky. When I tried to export the summary to Notion, the integration failed twice before it finally pushed the data. I had to refresh the page and reconnect my API token to fix it. It’s not a dealbreaker, but it’s definitely not the “it just works” experience the marketing copy promises.
Regarding API cost comparison for batch processing, you have to decide what your time is worth. Fireflies costs a monthly subscription fee, while hitting the Claude API for every meeting transcript is cheaper in raw dollars but more expensive in terms of developer time and manual overhead. I’d stick to the subscription if you aren’t comfortable managing API keys and JSON parsing.
Pros, limits, and the breaking point
The biggest pro is the integration ecosystem. Fireflies connects to Slack, Notion, and HubSpot, which saves me from manually updating project boards. It works flawlessly for meetings under 90 minutes. However, it hits a wall once you cross the 2-hour mark. In one 3-hour marathon session, the transcript started dropping chunks of text, and the summary became a generic, high-level blur that was useless for actual work.
You need to be realistic about the limits. When I fed it a very complex, technical discussion about architecture diagrams, it failed to interpret the context. It captured the words but missed the intent. It’s essentially a really smart parrot, not a project manager who understands the nuance of your specific team dynamics.
Which one should you actually buy?
If your goal is to save time, Fireflies is the winner. Looking at my data, I saved roughly 35 minutes per meeting by not writing summaries from scratch. If you are doing five meetings a week, that is nearly three hours of your life back. However, if your work requires high accuracy for dates and specific technical requirements, you cannot trust the summary blindly.
I recommend using Fireflies for the bulk of your internal syncs where a “good enough” summary is fine. For high-stakes meetings where every detail matters, use Fireflies to record, but copy the transcript into a more capable model like Claude 3.5 Sonnet to handle the actual analysis. That hybrid approach gives you the speed of automation without the risk of hallucinated deliverables.
One final note: keep an eye on your storage limits. The Pro plan has a limit on how many hours you can process, and I hit it during a busy week in October. If you have a high volume of meetings, check the tiers before signing up. Otherwise, you’ll be stuck manually uploading files anyway, which defeats the purpose.
Bottom line? Use it for the grunt work, but keep your brain switched on for the review. No AI is good enough to replace your own ability to verify if a task was actually assigned or if the AI just liked the sound of a deadline. Your mileage may vary, but sticking to this routine has kept my meeting headache at a manageable level for the last month.
