Based on my analysis of their documentation and network traffic during my testing, Fireflies.ai primarily uses a streaming architecture for its core recording functionality. The local recording option is limited and not the default behavior for most integrations.
Here is a breakdown of the data flow based on their system's design:
**1. Primary Method: Server-Side Streaming**
When you use a Fireflies.ai meeting link, browser extension, or calendar integration, the audio is streamed directly to their servers for processing.
* The client (your browser or the extension) captures the audio via the microphone.
* This audio data is encoded and sent via WebSockets or a similar persistent connection to Fireflies's cloud infrastructure.
* Processing (transcription, speaker separation, AI analysis) occurs server-side. The local machine does not perform these resource-intensive tasks.
* This is evident from the fact that if your internet connection drops during a meeting, the recording will have gaps or fail entirely.
**2. Limited Local Recording Scenario**
The "local recording" capability exists in a specific, controlled context:
* It is available **only** when using their desktop application (not the web app or extension).
* Even then, it functions more as a buffer. The application may temporarily store audio data locally to handle network fluctuations, but it still transmits the data to their servers for processing. A truly local-only processing mode is not provided, as their value proposition relies on their cloud-based AI models.
**Key Evidence from API and Network Analysis:**
When inspecting the traffic, you'll see calls to endpoints like `wss://api.fireflies.ai/...` or ` https://api.fireflies.ai/v1/audio/stream`. The client sends audio chunks, often in formats like Opus or PCM, encapsulated in a protocol buffer or JSON payload. There is no observable endpoint for simply returning a finalized, local transcript file without the data first traversing their network.
```
// A simplified conceptual view of the client-side streaming logic
async function streamAudioToFireflies(mediaStream) {
const audioContext = new AudioContext();
const source = audioContext.createMediaStreamSource(mediaStream);
const processor = audioContext.createScriptProcessor(4096, 1, 1);
const websocket = new WebSocket('wss://api.fireflies.ai/ws/ingest');
processor.onaudioprocess = (e) => {
const audioData = e.inputBuffer.getChannelData(0);
// Encode and send chunk via WebSocket
websocket.send(encodeAudioChunk(audioData));
};
source.connect(processor);
processor.connect(audioContext.destination);
}
```
**Security and Privacy Implication:**
This architecture is fundamental to consider for compliance. Since audio is streamed to and processed on their servers, you must rely on Fireflies.ai's data protection policies, encryption in transit, and processing agreements. There is no offline-only mode.
For a beginner, the practical answer is: assume all audio is streamed to their servers. The system is designed as a cloud service, not a local application. If your use case requires all processing to remain on-premises due to strict data sovereignty rules, this would be a significant architectural constraint.
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