Solution
For equity research and investment analytics teams: every earnings call structured into your own taxonomy minutes after the transcript publishes, with each value tied to its passage.
The problem
Reading a transcript end to end costs about an hour. In earnings season, across a coverage universe, most calls simply go unread — and everyone knows it.
Off-the-shelf tools return sentiment and named entities. Your analysts track specific facts, figures, and statements — fields no generic model was built to extract.
Manual notes differ analyst to analyst and quarter to quarter, so every cross-call comparison starts with reconciling formats before it produces a view.
The product, not a promise
How it works
The client's taxonomy is set up once; extraction models train on publicly available transcripts.
Each new transcript is picked up automatically as it is published.
The platform structures the transcript into the client's expected format in minutes.
Records are grouped by the customized taxonomy, call by call, company by company.
Who it's for
Equity research analyst
Head of research
Data & platform lead
A large investment banking company put it plainly: earnings call transcripts are a treasure trove of investment-grade information, and almost none of it gets used. The tools on the market read a transcript and hand back sentiment scores and a list of named entities — highlighting, when what the client needed was extraction into a well-defined taxonomy of their own design: the specific facts, figures, and statements their analysts actually track, structured the way their downstream systems expect.
The client’s taxonomy was configured in Botminds as a one-time setup, and extraction models were trained on publicly available transcripts, so work started without waiting on internal data pipelines. From then on, every new transcript is abstracted automatically: the platform reads it, pulls the fields the taxonomy defines, and delivers a structured record in the client’s expected format within minutes of the transcript becoming available.
Records are grouped by the customized taxonomy, so analysis across calls and across companies works from consistent, comparable data. An analyst comparing management guidance across three quarters looks at three aligned records instead of three hour-long transcripts.
Structured data from an earnings call feeds real investment decisions, so provenance is mandatory. Every extracted value ties back to the passage it came from, which makes verification fast — check the sentence, not the whole call. And because the taxonomy belongs to the client, the output is their own analytical framework, applied consistently at machine speed and reviewed by the people who own the decision. The platform does the reading; the desk keeps the judgment.
Objections, answered
Every extracted value ties back to the passage it came from, so verification means checking one sentence instead of re-reading the call. Ambiguous statements — hedged guidance, qualified figures — arrive flagged for the analyst's judgment rather than silently normalized.
The taxonomy is yours and is configured as a one-time setup: the specific facts, figures, and statements your analysts track, structured the way your downstream systems expect. The output is your analytical framework applied consistently at machine speed.
Extraction models train on publicly available transcripts, so setup starts immediately without waiting on internal data pipelines or exposing proprietary research. Your taxonomy definitions shape what gets extracted; your analysts' review confirms it.
Taxonomy configuration is a one-time setup measured in weeks, since training uses public transcripts. From then on, each new transcript is picked up automatically as it publishes and delivered as a structured record within minutes.
Watch the transcript structure into your fields in minutes — every value cited to its sentence, with one ambiguous statement routed to you.
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