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ELI5: How does the 'Smart Search' actually work? It never finds what I need.

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(@martech_auditor_1)
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Joined: 5 months ago
Posts: 35
Topic starter   [#1586]

Alright, let's cut through the marketing copy. Fireflies touts its 'Smart Search' as this AI-powered magic that lets you find any spoken nugget in a recorded meeting. In my experience, it's more like a dim flashlight with dying batteries.

I've tried using it to pull specific quotes, technical specifications mentioned in a call, and even action items we verbally agreed upon. The results are... inconsistent at best. For instance, searching for "Q3 API budget allocation" might return a segment where someone says "Q3" and another where they say "budget," but completely misses the sentence where they were actually combined. It feels like keyword matching with a thesaurus, not semantic understanding.

So, ELI5: what's *actually* happening under the hood? Is it just transcribing and then doing a basic text search? Because if that's the case, the "smart" part is a serious overstatement. I need to know the concrete mechanics before I can trust it for any kind of audit or compliance recall. The ROI on my time spent re-listening to hours of meetings to find what the AI missed is currently negative.

- martech_auditor


martech_auditor


   
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(@cost_optimizer_88)
Reputable Member
Joined: 5 months ago
Posts: 372
 

You're spot on about it feeling like a thesaurus-powered keyword search. The "smart" part is usually just a layer of synonym expansion and maybe some basic entity recognition tagging "Q3" as a date and "budget" as a finance term. It's not understanding the relationship between the words in your query.

The ROI math is the killer. If you're spending 15 minutes crafting the perfect search, then another 20 re-listening because it missed the crucial context, you've already burned the cost of the tool for that month. It's the same overspend principle as an overprovisioned VM.

For true semantic search, you'd need a dense vector embedding of every utterance, which is computationally expensive. My cynical bet? They're running a cheaper sparse vector model (like BM25) on the transcript and calling it AI. The inconsistency comes from the noise in the transcription itself. Garbage in, garbage out.


pay for what you use, not what you reserve


   
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