Cross linking builds research graph

Cross linking turned raw transcript volume into a usable research graph. The important move was not fancy extraction, it was making every company mention inside a call resolve to a real company page and the exact moment in the transcript where it was discussed. That let Tegus turn an hour long call into many reusable research entry points, which made the library feel broader, faster, and more valuable without needing perfect AI first.

• This mattered because Tegus was selling a subscription library, not mainly one off calls. A transcript became more valuable when it could support research on every company mentioned in it, not just the company that originally motivated the interview.
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• The workflow was concrete. Tag Zillow, Glassdoor, or Indeed in the text, map each name to the right company record, then deep link into the relevant 30 seconds instead of forcing an analyst to skim an entire hour. That is simple NLP, but it sharply improves discovery.
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• This is also why integrated research platforms win. Tegus was already trying to link transcripts with earnings call transcripts, SEC filings, and Canalyst models. Cross linking is the connective tissue that makes separate datasets behave like one product instead of a folder of documents.
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The next step is moving from links to answers. Once every mention is attached to a company, topic, and timestamp, summarization, theme extraction, and question answering become much more useful because the model can pull from a structured map of the library instead of a pile of disconnected transcripts.