Pioneering Data Sourcing with Cutting-Edge Technology
Mapping Corporate Disclosures to Global Reporting Frameworks
Our approach begins by deeply analyzing the core metrics and disclosure requirements found in leading global reporting frameworks — including financial standards (e.g. IFRS, US GAAP), regulatory requirements (e.g. SFDR, EU Taxonomy) and sustainability frameworks (e.g. ISSB, GRI, TCFD). By understanding the intent and structure of each framework, we ensure that every financial and ESG field we source is precisely defined and contextually grounded. The art of crafting a data product lies in striking the perfect balance between granularity, coverage and usability. Our mission is to provide data that is not only rich in detail but also immediately actionable and comparable across time and entities.
Exhaustive and Timely Collection of Corporate Disclosures
Our technology infrastructure uses advanced web emulation and large-scale automation to contiuously scan thousands of corporate websites at predetermined intervals. Every disclosed document, spreadsheet, or web page is processed and indexed with precision. This automated process is reinforced by a team of specialists skilled in navigating complex web architectures and websites with restrictive terms of use. This dual approach ensures strict compliance with legal standards while keeping our datasets consistently up to date. For clients who need to know first, our process can be accelerated to near real-time, delivering data updates within minutes of public disclosure.
Advanced AI for High-Precision Data Collection
Our extraction pipeline is powered by a multi-layered AI architecture designed for precision. Computer vision models convert diverse file formats — PDF, HTML, XLS — into structured, machine-readable data. Next, natural language processing (NLP) algorithms curate, translate, enrich, and rank content, preparing it for specialized data extraction agents and tabular deep learning models. This multi-stage approach ensures that every value we deliver is both technically accurate and contextually correct. It allows Tracenable to transform unstructured corporate disclosures into high-quality, ready-to-use financial and ESG data.
Human-in-the-Loop Verification for Maximum Accuracy
Even the most advanced AI systems have limits. To ensure complete accuracy, Tracenable employs a rigorous human-in-the-loop verification process that combines automation with expert review. Each extracted data point receives an accuracy and completeness score, guiding our analysts during validation. Two independent reviewers verify each source, and any discrepancies are escalated to senior analysts responsible for reconciliating the differences. This hybrid approach ensures continuous AI improvement and guarantees audit-ready data that is fully traceable to its original disclosure, meeting the highest standards of reliability and transparency.
Rigorous Quality Assurance for Unmatched Data Integrity
After compilation, every dataset undergoes an exhaustive quality control process to assess its reliability. Our QA strategy combines heuristic analysis with advanced machine learning (ML) techniques. Heuristic evaluations check for common irregularities, such as negative values or unit inconsistencies across reporting years. Simultaneously, our ML-based checks use unsupervised learning to detect anomalies through time-series analyses, distribution-based outlier detection, and clustering analyse. To complement these automated checks, we conduct daily manual audits and sanity reviews of randomly selected data entries. This multi-layered approach reflects Tracenable's total commitment to delivering data that meets the highest standards of quality and reliability.
