Keyword Search Was Never Designed for Conversations
Meetings are conversations, not documents. People do not use precise terminology when they talk. They say "that thing with the payments" instead of "Stripe integration refactor." They say "the issue Sarah raised" instead of "customer churn risk in EMEA."
Keyword search requires you to guess the exact words that were used. Semantic search understands what you mean.
How Semantic Search Works
Beaver AI indexes every meeting transcript using semantic embeddings — mathematical representations of meaning. When you search, the system finds content that is conceptually similar to your query, not just textually matching.
This means:
- Searching for "budget concerns" finds discussions about "cost overruns," "spending limits," and "financial constraints"
- Searching for "customer feedback" finds mentions of "user complaints," "NPS scores," and "support tickets"
- Searching for "hiring plans" finds conversations about "headcount," "recruiting," and "team expansion"
Why It Matters for Meeting Data
Documents are written deliberately. Their authors choose words carefully. Meeting transcripts are spontaneous — people use whatever words come to mind. This makes meetings especially poorly served by keyword search and especially well served by semantic search.
A keyword search for "deadline" might return 50 results. A semantic search for "projects at risk of being late" returns the 5 discussions where timing was genuinely a concern, even if nobody used the word "deadline."
What You Can Search
Beaver AI's semantic search covers:
- Full meeting transcripts
- AI-generated summaries
- Action items and their status
- Commitments and their follow-through
- Decisions and their context
Results are ranked by relevance and include links to the source material.
Search Your Meetings
Semantic search is built into every Beaver AI account. Start a free trial and find what you are looking for — even when you do not know the exact words.
