Criminal Behavior Model Powers Effective Patrolling Routes and Strategies

Optimized

Patrol route scheduling

Identified

Criminal trends & patterns

The Challenge

Police Patrols Were Scheduled Reactively, Not Based on Where Crime Was Likely to Occur

Avata Intelligence needed to develop a realistic, learnable model of criminal behavior to help urban police forces optimize patrolling strategies and resource allocation across the city.

Our Approach

ML Model Combines Crime Events, Demographic, and Environmental Data into a Behavior Model

Blindspot collected comprehensive crime-related data and built a machine learning model incorporating environmental and demographic factors alongside crime events. This analytics module was integrated into Avata's intelligence-led policing platform to enable data-driven decision-making for patrol scheduling.

Results

Patrol Routes Now Driven by Predicted Criminal Behavior Rather Than Intuition

More effective patrolling routes and strategies were implemented, enabling law enforcement to identify criminal trends and patterns and allocate resources based on predictive analytics rather than intuition.

"Blindspot created an analytics module for our intelligence-led policing platform, combining environmental and demographic datasets with crime events revealing trends and patterns of criminals."

Zaré Baghdasarian, CEO, Avata Intelligence

Generative AI Gives Mountain Gorillas a Voice in Global Conservation

Blindspot and AWS helped Tehanu deploy Claude 3.5 Sonnet via Amazon Bedrock to process gorilla behavioural data — identifying species-specific preferences at expert accuracy and enabling the first-ever digital financial transactions conducted by a non-human species.

Expert Accuracy on gorilla preferences

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

Next Steps

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