Solution
For data operations teams at market information providers covering municipal bonds: continuous crawling of new offering statements, 100+ attributes extracted per issue, verified values pushed straight to your database.
The problem
More than a million municipal bonds, each with 100+ page offering documents, and new issues and revisions published continuously — batch-driven manual collection never catches up.
Two reviewers reading the same offering statement did not always record the same terms, and non-standard clauses went undetected across the portfolio.
Format, layout, and legal language vary issuer to issuer, so template-based extraction tools fail exactly where the data matters most.
The product, not a promise
How it works
An intelligent crawler watches for newly published offering statements and revisions.
New and updated documents flow into the platform automatically — no manual collection.
Custom document-understanding models pull 100+ attributes from each 100+ page document.
Reviewers resolve flagged extractions, keeping output standardized across the portfolio.
Verified values land directly in the customer database through Botminds Data APIs.
Who it's for
Data operations analyst
Head of data operations
Data quality / client services
The municipal bond market is a data problem measured in millions. A major information provider needed more than 100 attributes extracted from each bond’s offering documents — documents that routinely run past 100 pages — across a universe of over one million bonds, with new issues and revisions published continuously.
Manual review teams produced inconsistent output: two reviewers reading the same offering statement did not always record the same terms, and non-standard clauses went undetected. Template-based automation failed for the opposite reason — the variation in format, layout, and legal language across issuers broke tools that expect data to sit in the same place on every page.
Botminds attacks both ends of the pipeline. On intake, an intelligent crawler monitors publication sources and loads the latest offering statements — including revisions — so coverage is continuous rather than batch-driven.
On understanding, custom document AI models were built from just 100+ examples annotated by the customer’s own subject-matter experts. The models learn what a call provision or a redemption schedule looks like across issuers, rather than where it sits on one issuer’s page. Extracted values are pushed directly into the customer’s database through Botminds Data APIs, so the data product updates without an export-import step.
An information provider sells trust. Every extracted attribute is cited to its source page, so a data question from a downstream customer can be answered by pointing at the document. Human reviewers stay in the loop on flagged extractions, and their corrections standardize output across the portfolio — the consistency the manual process never achieved. The result is a bond dataset that keeps pace with the market’s publishing volume, at an accuracy bar reviewers can prove.
Objections, answered
Every attribute is cited to its source page, so a data question from a downstream customer is answered by pointing at the document. Flagged extractions route to human reviewers, and their verification is recorded with the value.
The models learn what a call provision or a redemption schedule looks like across issuers, rather than where it sits on one issuer's page. Custom models are built from 100+ examples annotated by your own subject-matter experts.
Verified values push directly into your database through Botminds Data APIs — no export-import step. The crawler keeps intake continuous, including revisions, so the dataset updates as the market publishes.
Your SMEs annotate on the order of 100+ examples per document type inside the platform; from there, extraction runs and review begins. Most data teams are verifying live extractions within weeks, not quarters.
Watch 100+ attributes come out cited to their pages live in the demo.
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