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A working sandbox. No sign-up, no project. Sample data only.
- Searches by meaning, not just keyword. Finds the line even when you can't recall the exact phrasing.
- Concept extraction surfaces recurring themes across hundreds of hours of material.
- Chat with your archive: describe the idea and the chat locates the moment, regardless of exact phrasing, surfacing causality and connections you would otherwise miss.
- Indexing runs in the background as new transcripts land. No manual step needed.
Try a preset query or type your own.
How it works
Three steps from raw material to result.
Each transcript is broken into passages and indexed for meaning as soon as it lands. The project archive grows automatically with every new file.
Type the idea, not the exact words. The search ranks every passage in your archive by how closely it matches what you're looking for.
The numbers stopped making sense. That was the moment we knew.
Every result carries the file, timecode, and speaker. Click any hit to open the player at that line.
Frequently asked questions
How is this different from keyword search?
Keyword search needs the exact word. Semantic search ranks lines by meaning. A query like "when the numbers stopped making sense" finds "the math wasn't adding up" too.
What's under the hood?
Each transcript passage is encoded into a meaning-based index. Your query is encoded the same way and ranked by how closely it matches, with a small reranking pass on the top results.
Can I search across all my projects at once?
No. Semantic search is scoped to a single project to keep results focused and to respect collaborator boundaries. Use the project switcher to move between archives.
Do I need to re-index when I add a file?
No. New transcripts are indexed automatically in the background as soon as they land. You'll see a small progress indicator on the project page until indexing completes.
Is there a chat interface on top of search?
Yes. You can ask questions about your project and get answers grounded in cited transcript passages, with every citation showing speaker, file, and timecode.
Related capabilities
Further reading
Background guides and comparisons.
Embedding-based semantic search finds passages by meaning rather than by matching words. Here is what an embedding is in plain terms, how the search works, and where it still misses things.
Retrieval-augmented generation answers questions about indexed interview transcripts by retrieving relevant chunks and grounding the model's output in those chunks. Here is how the pipeline works and where it still fails.