21 August 2026
Searching a year of meeting transcripts is a different task than searching one, and the three tools that promise to do it — keyword search, semantic search, and asking a question directly — return three different things for the same words. The one you need depends on whether you remember the exact phrase, the rough idea, or just the fact you're trying to confirm.
A single meeting transcript behaves like a memo: skim it, ctrl-F it, done. A year of transcripts behaves like a corpus — hundreds of documents, no shared vocabulary, no guarantee the thing you're looking for was called what you'd call it. A team running four meetings a day for a year produces something like a thousand hours of speech and several million words. Full-text search across that returns a wall of hits for any common term and nothing for the one you actually need.
The tools built for the document — find-in-page, a transcript's own search bar — don't survive the jump to the corpus. What replaces them is three different retrieval methods, and each one answers a different question about what you typed.
Keyword search does exactly what it says: it matches the string you typed against the words on the page, across every transcript in the index. It's fast, it's precise, and it's the right tool when you know the exact term — a product name, a client's name, a number that was said out loud.
It fails the moment the word you're searching for isn't the word that got said. If someone promised a '99.9 percent SLA' and you search 'uptime guarantee,' keyword search returns nothing, even though the meeting exists and the promise is right there. It also inherits every transcription error underneath it — a mangled acronym or a misheard product name breaks the match before the search even runs.
Semantic search doesn't match strings, it matches meaning. Both the query and every chunk of every transcript get converted into a vector — a list of numbers representing where that piece of text sits in a space built from meaning rather than spelling — and the search returns whichever chunks sit closest to the query's vector. Search 'uptime guarantee' and it can surface the passage where someone said 'we can commit to 99.9 percent' even though no word matches.
The cost is precision. Semantic search returns what's conceptually adjacent, which means a query about 'cost' can just as easily surface a chunk about 'budget,' 'pricing,' or 'headcount' from a completely unrelated meeting. It's the right layer when you don't know the wording. It's the wrong layer when you need the exact sentence, because it will hand you five plausible ones instead.
Asking a question directly — 'did we ever promise Meridian a 99.9 percent SLA?' — isn't search with extra syntax. It's a system that runs a search underneath, usually a blend of keyword and semantic retrieval, then reads the passages it pulled back and writes an answer that cites where it came from. Search hands you ten transcript fragments to read. A direct question hands you the sentence and the date.
This is the layer most people actually want, because most of what gets searched for isn't a phrase, it's a fact — did we agree to that, who said it, has anyone brought this up before. Getting a synthesized answer instead of a results page is the difference between finding the meeting and getting the answer.
This is the actual friction, and it's the same problem in every method above: people don't say things the way you'd search for them later. A rep says 'we're flexible on net terms,' you search 'payment schedule.' An engineer says 'we'll eat the migration cost,' you search 'budget for migration.' Nobody speaking in a meeting is thinking about how someone will look it up eleven months later.
Keyword search has no defense against this — it needs the literal word. Semantic search absorbs some of it, because 'flexible on net terms' and 'payment schedule' sit close together in meaning space even with zero words in common. Asking a question absorbs the rest, because the system can try several phrasings of your question before it searches, ones you never typed.
AVAY keeps a transcript of every meeting it sits in and makes all of them searchable from any later meeting or from outside one, so a question asked in this week's renewal call can pull an answer from a call held eight months ago without anyone remembering which one it was. It runs the blended retrieval described above rather than a pure keyword match, which is why a rephrased question still finds the right passage.
It only knows what happened inside AVAY. A commitment made on a call run through a different tool, or in a hallway conversation nobody recorded, isn't in the corpus and can't be searched into existence — the machinery here is about finding what was said, not reconstructing what wasn't.
| Matches on | Best for | Breaks when | |
|---|---|---|---|
| Keyword search | Exact words and phrases | You know the term used | Wording differs from what was said |
| Semantic search | Meaning and topic proximity | You know the idea, not the words | Query is common enough to match the wrong meeting |
| Asking a question | A synthesized answer with citation | You want the fact, not the transcript | Question is too broad to retrieve a clear passage |
No. Search returns passages that match your query and leaves you to read them; asking a question runs that search internally and hands back a written answer with a citation to where it came from. The first gives you material, the second gives you a conclusion.
Because it matches on meaning rather than exact words, and two unrelated meetings can discuss conceptually similar things — pricing, staffing, a delay — using overlapping vocabulary. It's a trade made for catching rephrased queries; the fix is confirming the passage with a keyword check once you've found a likely candidate.
Only if they were recorded or transcribed somewhere the search tool can index. Nothing retroactively transcribes a conversation nobody captured, so the corpus starts wherever your recording actually started, not wherever your memory does.
Finding which meeting is a lookup — you already suspect the fact exists and want the source. Searching across all transcripts is the broader case, where you don't know if it was said at all, in what meeting, by whom, or in what words, and the search has to work without any of those anchors.
Keyword search breaks immediately, since it needs the literal string that's now wrong on the page. Semantic search is more forgiving because it can still match on the surrounding sentence's meaning even when one word in it is garbled, which is one reason it's worth trying before giving up on a query.
Keyword search needs your exact words, semantic search needs your rough idea, and asking a question needs neither — it's usually the fastest way into a year of transcripts because it does the rephrasing for you.
AVAY is a video meeting platform that transcribes the call itself — no bot joins, because there is nothing to join. Start one at avay.ai, read how each part works in the documentation, or see what it costs.