Your archive finally has a search bar.
Conversent turns audio and video libraries into structured, queryable data — so your team can find a moment, a quote, a person, or a topic in seconds instead of listening for it.
Oct 10, 2008
Evening Report
Lehman Brothers collapse special
▶ 14:22Feb 3, 2009
Sunday Interview
Sit-down with a Treasury official
▶ 08:51Sep 15, 2018
Evening Report
Ten-year retrospective roundtable
▶ 32:10The blind spot
Audio and video are opaque.
A library of 10,000 hours is a library nobody can search. To find a moment, a quote, a person, or a topic, someone has to sit down and listen — and that bottleneck is why most archives sit unused.
What we build into your archive
Six layers of structure, one searchable library.
Transcription alone isn't comprehension. Conversent combines your raw content with its production context, so every result carries meaning, timing, speaker, and rights.
Semantic search
Meaning is mapped to moments, so a plain-language question surfaces the exact passage inside hours of audio — not just a keyword match.
Speaker ID & consent gating
Patent-pending voiceprint matching maps consenting talent to the moments when they spoke.
Millisecond alignment
Every word synced to the instant it was spoken, producing caption files you can upload straight to YouTube, TikTok, and Spotify.
Music identification
Third-party music is detected and flagged, so it can be isolated or excluded before content leaves your walls.
On-demand packaging
The archive is broken into granular elements — audio, transcript, speaker, timestamp, rights — and re-shaped for whatever the request needs.
Transcription that learns
Custom models trained on your own programs get sharper with every correction, leaving 20–40% fewer errors to fix by hand.
What it unlocks
One index. Six things your team stops waiting on.
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Editors find clips in seconds
Pull the exact soundbite without scrubbing a timeline.
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Research becomes self-serve
Anyone in the building can query the archive directly.
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Audiences find what they want
Search by moment, topic, speaker, or scene across the catalog.
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AI answers cite you
Structured moments let chatbots ground replies in your work — and link back to it.
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Brand risk surfaces early
Know what's inside a show — and who said it — before it ships to advertisers.
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Rights are tracked at the source
Clearance, consent, and third-party audio are attached to the segment, not a spreadsheet.
Raw audio vs. structured data
Structure is where the value lives.
Raw files can't be searched, cited, or licensed with confidence — every use still owes the same downstream labor bill. Your production process already generates the context that fixes this. Conversent captures it.
Raw archive
Opaque by default
- Finding anything means someone listens to it
- Rights and consent live outside the content
- Third-party music sits undetected in the mix
Structured archive
Queryable like a database
- Any moment retrievable by meaning or speaker
- Consent and clearance attached to the moment
- Ready to answer licensing requests
How it works
Three steps from raw media to a search bar.
01
Connect the archive
Point us at your media — audio, video, podcasts — along with whatever production context you already keep: rundowns, transcripts, talent agreements.
02
We structure it
Transcription, alignment, speaker ID, embeddings, music detection, and rights flags are applied across the library and attached at the segment level.
03
Your team queries it
Ask a question in plain language and get back the exact moments, timecoded and rights-aware, ready to clip, cite, or license.
Get started
Let's make your archive searchable.
We'll walk through a live demo on a sample of your own content and show you exactly what comes back.