AI App Extracts ESG Data from 400+ Stores & 2,000+ Suppliers with 99%+ Accuracy

Context

An Italian luxury fashion house engaged a technology partner to deliver an AI-powered application to ingest relevant ESG data for calculation of ESG KPIs across a global footprint. The scope covered more than 400 retail stores and over 2,000 industrial suppliers across the supply chain, spanning utilities, materials, and manufacturing. The objective was to centralize dispersed ESG evidence, compute traceable KPIs, and expose all insights through a generative-AI powered interface to support public ESG reporting and operational decision-making for a high-profile luxury brand.

Challenges

The initiative faced three interlocking challenges. First, the variety of source documents was extreme: utility bills, emails, supplier purchase orders, certificates of material origin, invoices, and photographic evidence from stores and factories. Second, ESG reports are subject to intense public scrutiny and regulatory scrutiny, so extraction accuracy and auditability had to be exceptionally high. Third, every calculated KPI needed full traceability so that any public metric could be traced back to its originating data point and supporting documents. Additionally, the client wanted to avoid the ongoing expense and slowness of outsourcing data extraction to expensive consultancies, and needed to meet a strict legal reporting deadline for their first consolidated ESG filing.

Implementation

The team built a modular, AI-powered data platform comprised of multiple ingest pipelines, verification layers, and an interactive front end. A variety of AI-powered pipelines were created to handle the extreme heterogeneity of sources: document OCR and layout parsers for utility bills and certificates; NLP extractors tuned for supplier emails and purchase orders; and vision models for photos of labels and certificates. The system leveraged multi-modal (text and vision) and multi-model capabilities so the most appropriate extraction approach could be applied to each document type. An orchestration model automatically selected the correct pipeline for each incoming asset based on content-type classification and metadata.

To guarantee the high level of accuracy and traceability required, the platform included verification pipelines and a self-critic mechanism that assessed confidence in each extracted data point and raised alerts when confidence thresholds were not met. Workflows were implemented so a human reviewer could triage alerts, correct or approve data, and add contextual notes; audit trails preserved who approved each change. Full data lineage was implemented end-to-end so every KPI calculation could be traced back to the raw ingested file, the extraction output, and any manual adjustments. Our Fractional Head of AI implemented the solution architecture and governed the model validation lifecycle.

All ingested data points and the calculated ESG metrics were exposed through a generative-AI powered chat interface that can answer natural-language questions and produce advanced visualizations on demand. Example queries supported by the interface included: How much of my electricity consumed last year in Europe is renewable?; Which stores offer the biggest opportunity to improve my renewable energy use?; From which country is most of my silk coming?; Who are my top suppliers in the US for ready-to-wear?

Results

The solution successfully extracted relevant ESG data across more than 400 stores and 2,000 suppliers worldwide and provided the client with fully traceable KPI calculations. Extraction accuracy reached 99%+ on validated data points, significantly reducing manual rework and enabling the team to accelerate the creation of the first consolidated ESG report to meet legal deadlines. By retaining extraction and analytics in-house and automating verification and review workflows, the client avoided the recurring cost of expensive consultancy engagements. The generative-AI chat interface improved access to insights for non-technical stakeholders and made it straightforward to identify high-impact opportunities—such as stores with poor renewable energy usage or suppliers concentrated in specific countries—while preserving a complete audit trail suitable for public reporting and regulatory review.