deep_search flips this: you describe the idea, and Augent finds the exact moments where products, recommendations, and protocols are discussed, even when you have no idea what they’re called. The names, the dosages, the timestamps, all surfaced from a natural language prompt.
Searching by meaning
deep_search doesn’t match words. It matches meaning.
Under the hood, it converts every segment of the transcript into a numerical fingerprint that captures its meaning. Your query gets the same treatment. Augent finds the segments whose meaning is closest to yours and ranks them by relevance.
Describe what you’re looking for in plain language and Augent finds the moments where that idea was discussed, regardless of the exact words.
Query: “supplements that improve energy and testosterone”
Finds:
- “The data on Shilajit is really compelling for men over 30” — ep-33, 34:22
- “Most people would benefit from Tongkat Ali” — ep-11, 1:12:05
- “If you’re feeling burned out, this is the first thing I’d add to your stack” — ep-07, 48:15
Real example: supplement research across 50 episodes
Take 50 episodes of a health podcast and rundeep_search with queries like “supplement that actually works,” “what I take every morning,” and “the one thing I’d recommend.”
What you get back would take weeks to compile by hand:
| Supplement | Benefit | Who It’s For | Daily | Episode | Timestamp |
|---|---|---|---|---|---|
| Shilajit | Testosterone, energy | Men 30+ | Yes | ep-33 | 34:22 |
| Tongkat Ali | Hormone optimization | Men, athletes | Yes | ep-11 | 1:12:05 |
| Ashwagandha | Stress, cortisol regulation | High-stress individuals | Cycling | ep-29 | 28:41 |
| Apigenin | Sleep onset | Anyone with sleep issues | Yes | ep-42 | 52:07 |
| Oregano Oil | Gut health, antimicrobial | Immune support | Yes | ep-15 | 18:33 |
| Turmeric | Anti-inflammatory, joints | General health, athletes | Yes | ep-22 | 41:09 |
“The data on Shilajit is really compelling for men over 30” — ep-33, 34:22
“Most people would benefit from Tongkat Ali” — ep-11, 1:12:05
“The literature strongly supports Ashwagandha for stress” — ep-29, 28:41
“If you have trouble falling asleep, Apigenin is the one to look at” — ep-42, 52:07Fifty hours of audio. Every recommendation pulled out, sourced, and timestamped. Minutes, not weeks.
Same pattern, any domain
The supplement example is one use case. Same approach works anywhere you have hours of content and don’t know exactly what to search for:| Domain | What you’d query | What gets surfaced |
|---|---|---|
| Product research | ”tool they swear by,” “product that saved them time” | Specific software, hardware, and services you’ve never heard of, with firsthand endorsements and context |
| Competitive intel | ”feature their customers love,” “what they do differently” | Positioning, strategy, and advantages mentioned in interviews, demos, and earnings calls |
| Legal & compliance | ”admitted fault,” “agreed to terms,” “changed their story” | Critical moments in depositions, hearings, and recorded calls |
| Hiring & culture | ”why they left,” “what made them stay,” “red flag in the interview” | Patterns across exit interviews, all-hands recordings, and candidate screens |
| Education | ”the key insight,” “most common mistake,” “what I wish I knew” | Actionable takeaways from lectures, courses, and tutorials without watching the full thing |
| Content repurposing | ”best quote,” “most controversial take,” “funniest moment” | Clip-worthy moments across an entire content library |
Finding what you didn’t know to search for
This is whatdeep_search is built for.
Keyword search only works when you already know what you’re looking for. But the most valuable content in any recording is often something you didn’t expect: a passing recommendation, an offhand insight, a product mention you’d never heard of. You can’t write a keyword query for something you don’t know exists.
With semantic search, broad queries surface specific moments:
| Query | What it finds |
|---|---|
| ”product that changed their routine” | A specific brand or supplement the host credits with a measurable result |
| ”tool they use every day” | Software, hardware, or workflow the speaker can’t live without |
| ”something most people get wrong” | Contrarian insights and corrections buried in long-form conversation |
| ”underrated strategy that actually works” | Tactics and approaches that aren’t mainstream but have proven results |
deep_search found it.
Instead of listening through hours of audio, you mine across it. You’re not searching for words. You’re searching for ideas — and finding ones you didn’t know were there.
Embeddings are shared and stored
The first timedeep_search runs on a file, it computes embeddings for every segment and stores them in memory. Every subsequent search on that file is instant, no matter how different the query. The expensive computation happens once.
These embeddings are also shared with chapters and search_memory (semantic mode), which use the same vectors. Run deep_search on a file, and chapters or search_memory on that file is free. The embeddings are already computed.
When to use deep search vs. keyword search
Both tools exist because they solve different problems:| Keyword search | Deep search | |
|---|---|---|
| Best for | Product names, brand names, proper nouns, specific phrases | Concepts, themes, ideas, exploratory queries |
| Query | "Ashwagandha" | "supplement that helps with stress" |
| Matches | Exact word occurrences | Semantically similar segments |
| Speed | Instant (string matching) | Instant after first run (embedding lookup) |
| Misses | Anything said differently | Nothing meaningful. If the idea is there, it finds it |
search_audio when you know the exact term: a product name, a brand, a person. Use deep_search when you know the idea but not the words, or when you want to discover what’s in the content without knowing in advance.
Tool Reference
Parameters, response format, and technical details