Generative AI Gives Mountain Gorillas a Voice in Global Conservation

Expert

Accuracy on gorilla preferences

First-ever

Non-human digital transactions

The Challenge

Understanding Gorilla Preferences at Scale Was Impossible with Manual Analysis Alone

Understanding and acting on the preferences of mountain gorillas in Rwanda's Volcanoes National Park at scale, while creating economic systems that benefit non-human species alongside humans.

Our Approach

Claude 3.5 Sonnet via AWS Bedrock Processes Gorilla Behavioral Data at Expert-Level Accuracy

Adastra implemented AWS services including serverless execution and storage infrastructure. The team deployed Anthropic's Claude 3.5 Sonnet model via Amazon Bedrock to process behavioural data from gorillas, synthesising academic and observational datasets to identify species-specific preferences.

Results

Expert-Level Gorilla Preference Analysis Enabled — and the First Non-Human Digital Transactions

The system successfully identified gorilla preferences at a level matching human expert accuracy, with significantly greater efficiency than manual analysis. This enabled the first-ever digital financial transactions conducted by a non-human species.

"Together with AWS we have made the first step in a multigenerational journey where AI can help us better understand the interests of other species — not just humans."

Jonathan Ledgard, Co-founder and CEO, Tehanu

Integrating Generative AI Helps Ataccama Become a Leader in the 2024 Gartner® Magic Quadrant™

Over nine months, Blindspot integrated LLM technology into Ataccama's data management platform — building a natural language interface for non-technical users and an AI co-pilot for bulk data quality rules — contributing to Ataccama's Gartner Magic Quadrant leadership position.

Gartner Magic Quadrant Leader 2024

Machine Learning Models Reduce Customer Churn at Socialbakers

Blindspot built multiple ML models to predict which customers were most likely to churn next month, enabling Socialbakers to proactively intervene with targeted retention strategies and measurably improve their retention rate.

Reduced Monthly churn rate

Criminal Behavior Model Powers Effective Patrolling Routes and Strategies

Blindspot collected crime and demographic data to build a machine learning model of criminal behavior, integrated directly into Avata's intelligence-led policing platform to help law enforcement allocate resources more effectively.

Optimized Patrol route scheduling

Next Steps

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