AymurAI: Structuring Judicial Data on Gender-Based Violence

Gender-based violence (GBV) remains a widespread issue across many legal systems, yet the data needed to understand and address it effectively is often fragmented, unstructured, or difficult to access. Court records, legal filings, and judgments contain valuable information about how GBV cases are reported, investigated, and resolved. However, much of this information exists in lengthy legal documents that are not easily searchable or analyzable at scale. This lack of structured data makes it difficult for policymakers, researchers, and civil society organizations to identify patterns in how GBV cases are handled within the justice system. AymurAI addresses this challenge by using artificial intelligence to extract, organize, and structure information from judicial records related to gender-based violence.

AymurAI uses natural language processing (NLP) techniques to analyze large volumes of court documents, including judgments, case summaries, and legal filings. The system identifies key information such as the type of offense, legal charges, case timelines, judicial decisions, and references to laws or precedents. By automatically extracting these elements from complex legal texts, the platform converts unstructured legal information into structured datasets that can be searched, categorized, and analyzed more efficiently.

One of the key contributions of AymurAI is its ability to increase visibility into GBV-related cases within judicial systems. When data from thousands of court records is organized in a structured format, it becomes easier to identify patterns such as case outcomes, delays in proceedings, regional trends, or the application of specific legal provisions. These insights can help researchers and policymakers understand systemic challenges, including gaps in prosecution, inconsistencies in legal interpretation, or barriers faced by survivors seeking justice.

The platform also supports evidence-based policy development and legal research. Structured judicial datasets enable analysts to study trends in GBV cases over time, compare outcomes across jurisdictions, and evaluate the effectiveness of legal reforms. Civil society organizations and advocacy groups can use these insights to support policy recommendations aimed at improving legal protections for survivors and strengthening accountability mechanisms.

In addition, AymurAI contributes to greater transparency and accessibility of legal information. By organizing complex legal records into structured and searchable formats, the system makes it easier for journalists, legal practitioners, and public institutions to understand how gender-based violence cases are handled within the justice system. This increased transparency can support informed public discourse and promote greater accountability within legal institutions.

Ultimately, AymurAI demonstrates how AI-driven legal analytics can help transform large volumes of judicial data into actionable insights. By structuring information on gender-based violence cases, the platform enables deeper analysis of legal processes, helping stakeholders better understand the challenges survivors face and supporting efforts to strengthen justice systems and legal responses to GBV.

For additional context and detailed documentation of this use case, please refer to pages 108-112 in the attached Casebook.