Solution
For equity research analysts, research operations leads, and heads of research: every source your team reads, indexed with your senior analysts' judgment and searchable with one query.
The problem
Complete research means reading filings, analyst reports, transcripts, news, and interview notes — many types, many formats, arriving constantly.
Index-based search returns every document containing a term. Deep research needs the paragraph that matters in a 200-page filing.
The instinct for what matters lives with senior analysts, applied one document at a time. Juniors reproduce it slowly, or miss it.
The product, not a promise
How it works
Every relevant public document is picked up and loaded automatically as it appears.
Analysts annotate what relevant means; deep-learning models encode that judgment.
Every document is indexed by models that carry the domain experts' knowledge.
One query runs across all document types and sources, ranked by learned relevance.
Who it's for
Research analyst
Head of research
Research operations / IT
A large investment banking company described the problem precisely. Complete research on a company means reading and understanding hundreds of current documents: filings, analyst reports, news articles, call transcripts, emails, interview notes — many types, many formats, arriving constantly. Simple index-based search finds words, and deep research needs meaning. And the judgment of senior analysts — the instinct for which paragraph in a 200-page filing actually matters — lived only in their heads, applied one document at a time.
Botminds changed the setup in two moves. First, intake became automatic. Every new publicly available document in scope is monitored and loaded as it appears, so the research base is always current without anyone chasing downloads.
Second, and more important, the platform learned what relevant means to this team. Subject matter experts annotated documents in an initial training phase, and deep-learning models encoded that judgment into the index itself. Every document is indexed with the domain experts’ knowledge built in — so a search returns what an experienced analyst would have flagged, ranked accordingly, rather than everything containing a keyword. One query runs across every document type and every source.
Research that feeds investment views has to be defensible. Because every result links to its source document and passage, an analyst can verify a finding in seconds rather than re-reading the file. And because relevance comes from the firm’s own annotated judgment rather than a black-box score, the team can inspect, correct, and retrain it. The models scale the experts’ knowledge; the experts stay the authority on what it should be.
Objections, answered
Index-based search matches words. Here, subject matter experts annotate what relevant means in an initial training phase, and deep-learning models encode that judgment into the index — so a query returns what an experienced analyst would have flagged, ranked accordingly, across every document type at once.
That is the design. Your senior analysts annotate documents, the models are trained on those annotations, and the ranking reflects your desk's judgment rather than a generic score. The team can inspect results, correct them, and retrain — the experts stay the authority on what relevant means.
Every result links to its source document and passage, so an analyst verifies a finding in seconds instead of re-reading the file. Because relevance comes from the firm's own annotated judgment, the ranking itself can be inspected and explained.
It starts with the sources your team already reads. Automated monitoring and intake come first, so the research base is current from the beginning; the annotation and training phase runs alongside with your senior analysts, and search quality improves as their judgment accumulates in the models.
Run one search across its filings, transcripts, and news — and watch the ranking follow analyst judgment, not keyword counts.
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