Automated extraction, verification, and analysis across thousands of sustainability
disclosures — all in one platform.
How it works
From report to insight, in minutes.
A single pipeline that turns raw sustainability disclosures into clean, verified, structured
data.
01—The Source
Every insight starts with a report.
Companies publish annual and sustainability reports packed with ESG metrics, narratives, and disclosures. The SRN Lab hosts the most complete database of public CSRD-compliant reports and we are growing our database every day.
Acme_AR_2025.pdf
Annual Report 2025
Scope 1 Emissions12,450 tCO₂e
Page 47 of 234PDF
02—Ingest & Embed
Reports become queryable knowledge.
Each document is chunked and embedded into a semantic vector store, linked to company metadata. Every section is instantly searchable and cross-referenceable across the entire universe of reports.
Vector Store · 3 documents
PDF
→
climatebiodiversitygovernance
own workforcesupply chainpollution
Chunks: 2,847 · Dim: 1,536
03—Define Schema
You decide what data you need.
Build a custom extraction schema — define fields, data types, and validation rules. If it appears somewhere in the report, we can extract and structure it, be it standardized financial data or unstructured ESG-related information.
Extraction Schema+ Add field
scope_1_emissionsnumberreq
scope_2_market_basednumberreq
board_diversitypercentopt
water_withdrawalnumberopt
employees_totalintegerreq
net_zero_target_|
04—AI Extraction
Our AI reads every page, for every company.
State-of-the-art extraction models run across your entire report universe, pulling structured data points with source references, using a mix of keyword-based search and semantic similarity to identify the right paragraphs.
AI Extraction · running94.2% conf.
Source · p.47
The company reported Scope 1 emissions of 12,450 tCO₂e for FY 2023. Board included 38% women.
Extracted
scope_112,450tCO₂e
board_div38%
employees24,300
05—Verify
Human oversight, built in.
Sample-verify AI extractions yourself or onboard a review team. Flag discrepancies, tune prompts, and adjust model settings — building a quality loop that compounds over time.
Review Queue94.2% ✓
✓scope_1_emissions12,450 tCO₂e
✓board_diversity38%
⚠water_withdrawal2.4M m³
✓employees_total24,300
J
T
A
3 reviewers active
06—Quality & Outliers
Catch what the eye misses.
Automated rule checks and outlier detection surface anomalies, data gaps, and inconsistencies across the full dataset before they reach downstream analysis.
Scope 1 Emissions · 8 companies
↑ outlier
⚠ 1 outlier detected · value 3.1× above median
07—Distribute
Trusted data, ready to use.
Export via REST API or download as a flat file CSV. Integrate into models, dashboards, or research workflows — fully traceable, with provenance back to the source.
