Client
Mid-sized automotive components manufacturer headquartered in Germany, supplying Tier-1/2 OEMs across the EU.
Context
With the EU Corporate Sustainability Reporting Directive (CSRD) ramping up, the client struggled to compile Scope 3 data from more than 300 suppliers. The ESG team spent weeks chasing files, normalizing formats, and fixing errors before every reporting cycle.
Challenge
- Fragmented submissions (CSVs, PDFs, spreadsheets) and inconsistent units.
- Manual checks for data quality (coverage %, units, ranges).
- Methodology text (“estimated”, “assumed”) hard to assess at scale.
- Tight deadlines; high audit rework risk.
Objectives
- Accelerate supplier data validation and consolidation.
- Reduce audit risk via clear, traceable issues & fixes.
- Free ESG analysts’ time for actual analysis, not chasing and formatting.
Solution Overview — Agentic AI Validator
We designed an Agentic AI system to decide and act: run deterministic checks, interpret methodology text, and draft precise correction emails — without hand-coding every branch.
- Rule-based validation: required fields, numeric ranges, unit = “kgCO2e”, coverage ≤ 100%.
- LLM-assisted review: reads methodology text, flags vague terms, drafts correction emails.
- Next-action policy: accept valid data, else request targeted corrections with deadlines.
Implementation Snapshots
- Week 1: Lightweight prototype, rule schema, LLM prompts.
- Week 2: Policy orchestration + email drafting, 15 core test runs.
- Week 3: AWS landing zone (CDK bootstrap), stack design (S3 → Lambda → DynamoDB).
Business Impact
- 60–75% faster validation turnaround for first batch of suppliers.
- Over 40% fewer audit corrections, thanks to unit/range checks.
- Analysts freed to focus on variance analysis and supplier coaching.
Sample Metrics (first 30 days)
- Supplier on-time submissions ↑ +32%
- Validation pass-rate on first attempt ↑ +28%
- Avg. time per file ↓ −55%
- Top causes flagged: unit mismatch, coverage > 100%, vague methodology.
Client Quote
“The agent’s emails were spot-on. Our suppliers finally knew exactly what to fix, and our team could focus on trends and material risks.”
Call to Action
Ready to validate supplier data at scale — without the manual grind?