Wildlife-Centered AI: How AWS and Tehanu Use Generative AI to Give Wildlife a Voice in Global Conservation

While Homo Sapiens (commonly known as the human race) share our planet Earth with 8 million non-human species, our attention and conservation efforts reach only a small percentage of this vast biodiversity. Many remain unstudied, with only 2 million documented by science, leaving the majority unacknowledged or forgotten. Animals, trees, and other species play vital roles in sustaining the global economy, which cannot thrive if resources benefit only humans. Tehanu believes that these species must actively participate in our global economic framework to ensure their survival and the planet’s prosperity.

In 2024, Tehanu sought to launch a project focusing on a family of mountain gorillas in Rwanda’s Volcanoes National Park, on the border with Uganda and the Democratic Republic of Congo. While mountain gorillas represent a rare success story in conservation, many efforts to protect other species suffer from fragmented research, limiting the ability to generate implementable findings. With the objective to automate the aggregation of academic studies, such as the gorillas’ behavioral preferences, Tehanu hoped to enable informed policymaking that would support biodiversity. This innovative approach aimed to improve intervention accuracy—such as suggested interests on anti-poaching measures and veterinary care—for mountain gorillas while reducing negative impacts on other species within their ecosystem.

Using generative AI to process and extract patterns from a comprehensive behavioral dataset on the mountain gorillas, the project set out to demonstrate how AI can uncover the nuanced needs and preferences of non-human species, translating them into actionable insights for human-centered economic systems. By integrating the interests of non-human species into market dynamics, Tehanu aims to create a sustainable model that fosters coexistence and mutual benefit between humans and the broader natural world.

The project’s success relied heavily on the scalability and efficiency of the AWS Global Infrastructure, which formed the backbone of a solution capable of processing extensive unstructured data. The team partnered with conservation experts to refine its system, ensuring the complex needs of mountain gorillas were comprehensively captured. Adastra helped synthesize data from hundreds of academic papers, and analyzed them using a structured pipeline developed by using Amazon Bedrock—the easiest way to build and scale generative AI applications with foundation models. This pipeline utilized Large Language Models (LLMs) to extract, validate, and categorize gorilla preferences. Adastra’s meticulous process of iterative refinement, expert review, and clustering ensured both the accuracy and transparency of insights. The team also conducted human-led inference experiments using an interview guide, followed by thematic analysis, to code the responses and derive key themes— finding that AI-generated preferences offered more operational recommendations than those generated manually.

Adastra implemented AWS services, including Amazon Bedrock, Amazon Simple Storage Service (Amzon S3), and AWS Lambda to support the seamless storage and serverless execution of generative AI features. Using Anthropic’s Claude 3.5 Sonnet model in Amazon Bedrock, Tehanu accurately identified gorilla preferences at a level comparable to human experts, but with significantly greater efficiency. This breakthrough in operational efficiency enables conservationists to analyze and respond to wildlife needs more comprehensively, accelerating conservation efforts that traditionally required extensive manual analysis and expert consultation. AWS also offered scalable compute resources, including Amazon S3, Amazon Bedrock, Amazon Lambda, and Amazon CloudWatch. These resources enabled Tehanu to manage databases for the Internet of Species, infer species interests, and maintain application programming interfaces (APIs) for digital monitoring, reporting, and verification (MRV).