hindcast

Datasets

Global media transcripts dataset (earnings, news, podcasts)

Spoken finance is where positioning is explained and hedged. A CFO's tone on the third analyst question, a central banker's aside at a conference, a commodity trader on a regional radio segment — none of it appears in a filing, and almost none of it is searchable in a form an agent can cite.

Hindcast is building a transcript layer across earnings calls, world news broadcasts, radio and finance podcasts: speaker-attributed, time-coded to the second, and chunked so retrieval returns a passage rather than an hour. Every extract carries the program, the speaker and the broadcast timestamp, which is what makes a quote checkable instead of merely plausible.

Language coverage is part of the point. A Portuguese-language interview about a harvest and an English earnings call about the same commodity belong in the same index, joined by the entity graph rather than by hope.

hindcast — session

preview

hindcast> find where management discussed pricing on the last earnings call

→ tool: transcripts_search { entity:"XYZ", type:"earnings_call", query:"pricing" }

matching passages with speaker and timestamp

source: call transcript, time-coded to the second

Preview of the shape of an answer — endpoints open to the waitlist first.

What's covered

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Role

Company

Datasets you need

Questions

Are transcripts machine-generated or official?

Where an official transcript exists it is preferred and marked as such; otherwise the transcript is generated and labeled, so an agent can weight a quote by how it was produced instead of treating all text as equally reliable.

Can I search across languages?

That is the intent — entities and topics are resolved across languages, so a query about a company returns relevant passages whether they were spoken in English or Portuguese.

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