Upload your calls, meetings, and recordings, and search 18 million episodes of public conversation alongside them. Audioscrape transcribes your audio, ties every quote to a speaker and a timestamp, and serves it to your agents over API and MCP.
or upload your own audio to transcribe. Free, no card.
Most tools give you a transcript, or search public content. Audioscrape is the layer underneath both: one question, answered across what your team said and what the world said.
Sales calls, support lines, interviews, field recordings. Transcribed, speaker-attributed, isolated to your workspace, searchable only by you and your agents, never used to train a model. Try an upload →
Podcasts, interviews, talks and public meetings, discoverable by title and topic — with over 100,000 hours fully transcribed, speaker-attributed and entity-linked, growing daily. Ground an answer, cite the source with a timestamp.
One search_audio_content() call spans both. Your agent doesn't care where the memory lives.
The same pipeline runs on your uploads and the public corpus. Nothing to configure.
Upload a file, point us at a URL, or search the corpus we continuously crawl. Any common audio format.
State-of-the-art transcription with speaker diarization and entity linking. Segments tied to a speaker, a timestamp, and the topics they mention.
Keyword + semantic search over the index, returning ranked, cited segments your agent can quote, over REST or native MCP.
Not a transcription API you post-process, not a meeting tool locked to one workflow. Audioscrape is the retrieval layer underneath both. Your agent gets ranked, attributed segments it can quote and cite.
Find a spoken claim and tie it to the named person, with a timestamp and source. Not "someone in this episode," but who.
Entity- and topic-aware search across the corpus and your uploads. The exact segments, not summaries.
Agents fan out related queries per entity, keyword, or semantic variant, then synthesize. Built to be called many times, fast.
Agents can even discover the layer on their own: we publish an llms.txt.
One endpoint returns ranked segments with speaker, timestamp, source, and score. Bearer-auth, pinned to your workspace. The MCP server wraps the same thing for tool-calling clients.
# search your uploads + the public corpus
curl "https://www.audioscrape.com/api/search" \
-H "Authorization: Bearer $AUDIOSCRAPE_KEY" \
-H "Content-Type: application/json" \
-d '{ "query": "interest rate guidance", "limit": 5 }'# Claude, Cursor, ChatGPT: add as a connector, sign in when prompted
connector URL https://mcp.audioscrape.com # OAuth handled by your client
# headless agents & CI: same server, API-key auth
{ "mcpServers": { "audioscrape": {
"url": "https://mcp.audioscrape.com",
"headers": { "Authorization": "Bearer ${AUDIOSCRAPE_KEY}" }
}}}# real response shape, truncated
{ "results": [ {
"speaker": "Jerome Powell", "score": 0.94,
"text": "We are not on a preset course; we will respond to the data...",
"timestamp": { "start": 724.1, "end": 731.8 },
"podcast": { "title": "FOMC Press Conference" },
"urls": { "segment": "https://www.audioscrape.com/podcast/..." }
} ] }Hard tenant isolation, your data never used to train models, a DPA, and a 99.5% SLA, with a path all the way to a dedicated single-tenant instance when you need it.
Forced row-level security by default; a single-tenant instance for buyers who require it.
Never used to train models. Forced row-level isolation, a DPA, export or delete on request. US or EU/EEA processing region; EU/EEA residency available.
Separate Dev / Test / Prod, per-env keys and quotas, transparent usage-based overage.
Real segments from the corpus: who said it, when they said it, one click to the audio.
Free to browse, no key. continuously ingesting, new episodes daily. Follow a person or a topic straight into the transcripts.
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Yes. Uploads live in your workspace, isolated by row-level security enforced in the database itself. Only you, your team, and agents holding your keys can search them.
No. Your audio and transcripts are never used to train models. They exist to answer your queries, nothing else.
Full search across the public corpus, API and MCP access, and your first uploads. No credit card required.
State-of-the-art speech models with speaker diarization. Every segment carries a timestamp and a link to the source audio, so any quote can be verified in seconds.
Your choice of US or EU/EEA processing region, pinned per workspace. Enterprise customers get a DPA and data-export or deletion on request.
A native MCP server plus a REST API; one key works for both. Claude, Cursor, ChatGPT, or anything that speaks MCP or HTTP.
Yes. It turns raw audio into a searchable, speaker-attributed record and serves it to AI agents. Three things set it apart: a public corpus of 18M+ episodes that is already indexed, so there is no pipeline to build; speaker identity that persists, so a voice enrolled once is named in every later recording; and delivery over MCP and REST, built for agents rather than dashboards.
A transcription API processes one file per request and returns text; the memory of who said what is your problem. Audioscrape keeps the record: your uploads and the public corpus in one searchable index, with named speakers, timestamps, and citations, queryable by your agents for as long as you keep it. Speaker enrollment, entity graphs, and alerts on spoken text come with it. Read the agent facts page.
Search 18 million episodes in the next three seconds, then connect your own audio and make it all one layer.