Abstract: A major challenge in combating illegal wildlife trade and protecting biodiversity in Latin America and Sub-Saharan Africa is the limited capacity of frontline practitioners to accurately identify wildlife species and derived products in the field. Frontline professionals, including customs officers and wildlife rangers, often encounter live animals, animal parts, and wildlife products without access to the expertise or tools needed for reliable identification, particularly in ports, border crossings, and remote areas. As a result, misidentification and delays are common, enabling illegal trade to go undetected and increasing risks to endangered species. This project addresses this need through the development of an AI-powered wildlife identification system that integrates computer vision with a fine-tuned large language model, enabling users to identify species and receive context-specific guidance on appropriate conservation protocols. By providing AI-powered tools and up-to-date conservation information, the system strengthens frontline capacity to detect wildlife trafficking and enhances biodiversity protection in regions most affected by illegal trade.