Solution
For ESG research teams, ratings analysts, and sustainable-investment groups: granular ESG metrics pulled from reports with no standard format, every figure cited to its source page.
The problem
There is no consensus format for sustainability disclosure. Each company reports what it chooses, where it chooses, in its own layout.
Analysts identify and download reports from scattered company sources, and each one demands its own intensive understanding before a single metric comes out.
A defensible rating depends on the specific metrics inside the disclosure — buried in data tables, footnotes, and paragraphs of prose.
The product, not a promise
How it works
Company sources are watched continuously; new sustainability reports are collected automatically.
AI reads each unstructured report — there is no standard format, and it does not need one.
Granular ESG metrics are pulled from wherever they appear in the document.
A custom summary is produced per company, every figure cited to its source page.
Who it's for
ESG analyst
Ratings / research lead
Methodology / audit
A reputed US-based financial institution, active across a wide range of industry ratings, needed ESG data at scale. The raw material is sustainability reporting — and sustainability reporting is a mess by design. There is no standard format and no consensus structure: every company discloses what it chooses, where it chooses, in its own layout. Analysts were identifying and downloading reports from multiple sources by hand, and each source demanded its own intensive understanding before a single metric could be pulled. Yet the rating depends on granular metrics — the specific figures that actually describe a company’s ESG position, well beyond a headline score.
Botminds automated the pipeline end to end. Source monitoring and document intake run continuously, so new sustainability reports arrive in the platform without anyone hunting for them. The AI reads each unstructured document as a document, independent of any template — which matters when no two companies report alike. Granular metrics are extracted from wherever they appear, whether that is a data table, a footnote, or a paragraph of prose, and a custom summary is produced for each company in the exact shape the ratings workflow needs.
A rating built on extracted numbers is only as credible as its evidence. Every figure the platform extracts is cited to its source page, so an analyst — or a client challenging a rating — can go from metric to original disclosure in one click. Low-confidence extractions route to human review rather than flowing silently into the score. The result is ESG data that is faster to produce and easier to defend, which for a ratings business is the point.
Objections, answered
Every figure is cited to its source page, so verification is one click from metric to original disclosure. Extractions the platform is less confident about are routed to human review rather than flowing silently into the score.
Yes — extracted metrics map to your taxonomy, and each company's summary is produced in the shape your ratings workflow already consumes. The platform adapts to your methodology; the methodology does not bend to the tool.
The trail runs from the rating input back to the exact page of the company's own disclosure. An analyst — or the client — goes from metric to source in one click, which turns a challenge into a lookup instead of a re-research exercise.
Source monitoring is pointed at the new coverage set and intake runs from there. Because reading is template-free, new companies with new report layouts need no per-company configuration — which matters when no two companies report alike.
Watch granular metrics come out of tables, footnotes, and prose — each one cited to its page.
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