14 August 2026
Searching one meeting's transcript works with plain keyword matching because you roughly remember what was said and Ctrl+F finds the phrase. Searching a year of transcripts fails with the same method, because the words you type are almost never the words anyone used when they made the decision you're trying to find. The fix is semantic search, which matches meaning rather than exact text, and it changes what you should type into the search box in the first place.
Inside a single transcript, keyword search works because you have context. You know it was Tuesday's call, you remember someone said 'budget' at some point, and you scan the handful of hits. That's a small search space and a decent human memory doing half the work.
Across a year of meetings, both of those crutches disappear. You don't know which of 400 meetings the decision was in, you don't remember the exact phrase, and a keyword index just returns every meeting where 'budget' appears — which, if your team talks about budgets often, is most of them. The problem isn't that search is slow. It's that keyword matching answers a different question than the one you're asking.
A keyword or full-text index tokenizes the transcript and matches on exact terms or their stems — 'delay' matches 'delayed' and 'delaying' but not 'push out' or 'slip.' It's fast, exact, and predictable, and it's the right tool when you know the specific word someone used, like a product name or an acronym.
Semantic search works differently. Each chunk of transcript gets converted into a vector — a numeric representation of its meaning — and your query gets the same treatment. The search then ranks transcript chunks by how close their vectors are to your query's vector, not by whether they share words. That's why a query like 'did we decide to push the launch' can surface a meeting where someone actually said 'let's hold Q3 and revisit in the fall' — no word overlap, same meaning.
This is the real reason cross-meeting search fails more often than single-meeting search: decisions in a live conversation get phrased around the topic, not stated as a clean sentence you could later search verbatim. Someone hedges, someone else agrees by saying 'yeah, let's go that way,' and the actual commitment lives three turns later when someone repeats it back differently.
Try reconstructing how a real decision sounded. You'd search 'pricing decision,' but the transcript says 'so we're keeping it at the current tier through renewal season' — no word 'pricing,' no word 'decision.' Multiply that gap across dozens of meetings and keyword search's recall collapses even though the information is sitting right there.
Search with the question you actually have, not the term you assume was used. 'Why did we drop the enterprise tier' will do better in a semantic system than trying to guess whether anyone said the word 'drop' or 'enterprise' or 'tier' at all.
It also means broader queries often outperform narrow ones across many meetings, which is the opposite of how keyword search behaves. A specific keyword query on one transcript narrows results; a specific keyword query across a year of transcripts often returns zero, because the narrower you get on wording, the less likely anyone used that exact wording. Ask the question in plain language instead and let meaning-based matching do the narrowing.
Meaning-based matching isn't free of failure modes. It can surface a meeting that's topically similar but not the one you meant — a query about 'the vendor contract' can pull in three different vendor conversations that all sound alike in vector space but concern different vendors. You still have to check the source, not just trust the top result.
It also inherits whatever's wrong with the underlying transcript. If the transcription missed a word, mangled a name, or attributed a line to the wrong speaker, no amount of semantic matching recovers information that was never captured correctly in the first place. AVAY's transcription runs on browser speech recognition, which is solid in Chrome and Edge but not guaranteed elsewhere — that's worth knowing before you build a search habit around it, because search quality is bounded by transcript quality.
AVAY indexes what was said across every past meeting, not just the current one, so a question like 'what did we agree with legal about the NDA clause' can be answered by pulling the relevant moment from a call three months prior, with the source meeting and timestamp attached rather than a bare quote. You can ask that question live and get it answered out loud, or search it after the fact.
The distinction matters most for teams that treat meetings as their record of decisions instead of writing everything into a doc afterward. If nobody's rewriting decisions into a wiki, the search has to work against however the decision actually got said — which is exactly the case where keyword search across meetings breaks down.
| What it matches | Good for | Fails when | |
|---|---|---|---|
| Keyword search | Exact terms and stems in the text | Names, acronyms, ticket numbers, one known transcript | The query wording differs from the speaker's wording |
| Semantic search | Meaning and similarity between query and transcript chunks | Questions about decisions, reasons, and context across many meetings | Topically similar but unrelated meetings get surfaced together |
No — keep keyword search for exact terms like product names, ticket IDs, or acronyms where you know the literal string was used. Use semantic search for anything phrased as a question about a decision, reason, or outcome, which is most of what people actually try to find across months of meetings.
Across many meetings, a narrow keyword-heavy query assumes you already know how the decision was phrased, which you usually don't. A broader natural-language question gives a semantic search system more meaning to match against, so it can find a paraphrase even when your exact words weren't used.
Only partially. If a name or key term was transcribed incorrectly, semantic search can sometimes still match on surrounding context, but a badly garbled chunk is harder to retrieve accurately no matter what search method you use. Transcript quality sets the ceiling for what search can find.
As far back as the transcripts exist and were indexed — there's no natural time limit to semantic search itself. The practical limit is usually whether older meetings were transcribed and stored at all, not whether the search can find them once they are.
Keyword search fails across a year of meetings not because it's slow but because it assumes you know the exact words someone used to make a decision — search by the question you actually have and let meaning-based matching find the paraphrase instead.
Meetings that take their own notes, in the browser: avay.ai.