Intelligent multi-language AI system for automated ESG data extraction from utility bills

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To streamline ESG disclosure, a Southeast Asian reporting platform needed to automate the extraction of carbon data from utility bills—despite inconsistent formats, varied languages, and fragmented inputs. Ekimetrics developed a custom GenAI and OCR-based solution capable of handling document diversity at scale, unlocking speed, accuracy, and multilingual flexibility for thousands of SMEs.

99% accuracy

in extracting multi-month metrics in under 30 seconds

Supports 50+ languages

across diverse bill formats

Significant reduction

in manual validation effort through AI-driven accuracy

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Ce que nous avons mis en place

Enjeux

Existing OCR engine was limited to one document type and a single carbon metric
No support for multilingual or multi-format document processing
Each new document type, metric, or language required high-effort manual retraining of models
Scaling across SME sectors and regions was constrained by costly and resource-intensive processes

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Notre approche

Designed a scalable GenAI-powered system to interpret and extract ESG-relevant data from a wide range of document types and languages, reducing dependency on rigid, template-based models
Implemented smart filtering and structuring logic to ensure only relevant, high-quality data is captured
Build and deployed the solution on a secure, cloud-based infrastructure ensuring seamless integration and enterprise-grade performance

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Résultats

Achieved near-perfect extraction accuracy (99%) significantly reducing manual effort—through the combined power of Azure AI and OpenAI for intelligent document processing
50+ language and multi-format support enabled by the flexible model architecture, unlocking adoption across diverse SME sectors and regional markets
Improved user experience via a drag-and-drop upload interface triggering real-time extraction, powered by the secure and scalable Azure deployment

Challenge

Our approach

Outcome

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