How OeKB Automated ESG Disclosures with AI: From Hours of Manual Work to Minutes of Intelligent Processing
An AI-powered form assistant that extracts, interprets, and verifies sustainability data from company reports in multiple languages and formats.
INNOVATING WITH AI
OeKB was standing in a very complex and difficult to fill out form. The ESG questions were challenging, important information was distributed in the text, tables and graphs. There was also inconsistent sources, reports would have very different structures, languages and qualities. Traceability was another problem as proposals must be verifiable (sources, pages, excerpts) in order to inspire confidence, and the last factor that made this project a must was the research had to be done manually, with lots of copy-paste, this from the companies interested on filling out this form, this was a very time consuming.
WE INTEGRATED AI TO HELP COMPANIES FILL OUT THE FORM IN THE MOST EFFICIENT WAY POSSIBLE. COMBINING SEVERAL SPECIALIZED MODELS, WITH A FOCUS ON QUALITY, TRANSPARENCY, AND MAINTAINABILITY.
THE RESULTS
The document processing, Breakdown of reports into text, tables, and graphic elements (including page references and metadata) to make each type of information optimally usable. We also added a RAG-Component, Intelligent context search that finds the most relevant text passages, table cells, or chart excerpts for each ESG question. Furthermore, we have also added a large language model formulates concrete, verifiable answer suggestions based on the context found, and lastly, we have also added VLM for visual components, which the vision language model interprets tables and graphics (e.g., emission bars, key figure tables) and provides structured values for the answer.
Why This Matters
ESG disclosures are mandatory. Companies have to do them. But the process was designed for humans, not data. By automating the research and extraction layer, we freed companies and ÖeKB to focus on what matters: making sustainable finance faster and more accessible.
The broader pattern: complex forms + scattered data + multiple formats = perfect use case for AI.
If your organization collects data through forms (applications, disclosures, compliance questionnaires), the same approach applies.
INNOVATING WITH AI
OeKB was standing in a very complex and difficult to fill out form. The ESG questions were challenging, important information was distributed in the text, tables and graphs. There was also inconsistent sources, reports would have very different structures, languages and qualities. Traceability was another problem as proposals must be verifiable (sources, pages, excerpts) in order to inspire confidence, and the last factor that made this project a must was the research had to be done manually, with lots of copy-paste, this from the companies interested on filling out this form, this was a very time consuming.
THE TECHNICAL CHALLENGES WE SOLVED
Document Fragmentation
Company reports aren't indexed for ESG questions Solution: Multi-modal breakdown transforms any document into a searchable knowledge base.
Cross-Language Complexity
Some reports are German, others English; some are bilingual Solution: Multilingual embeddings + LLM fine-tuning handles all languages transparently
Hallucination Risk
LLMs can make up plausible-sounding answers Solution: RAG (Retrieval-Augmented Generation) grounds every answer in actual source documents
Visual Data
Charts and graphs contain critical data (emissions trends, key metrics) that text-based models miss Solution: Vision Language Model interprets visual elements and extracts structured data
Audit & Compliance
ÖeKB needs a full audit trail: what data was extracted, from where, by whom, when Solution: Every extraction event logged with document reference, timestamp, user action, source citation
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