Data was distributed across multiple departmental systems, making it difficult to access and analyse comprehensively.
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VE3 partnered with UK local councils through the YPO framework to tackle complex data challenges in public service delivery. By implementing a Graph Retrieval-Augmented Generation (RAG) solution on AWS using Amazon Bedrock, Llama3, and Amazon Neptune, VE3 enabled faster, smarter access to policy and guidance information. This transformation improved operational efficiency, accelerated decision-making, and strengthened the overall quality of public services.
Data was distributed across multiple departmental systems, making it difficult to access and analyse comprehensively.
The councils needed a solution that was scalable to handle increasing data volumes and flexible enough to integrate with existing systems while supporting advanced AI capabilities.
Managing vast amounts of policy and guidance documents was cumbersome, impacting the councils' ability to make informed decisions quickly.
The councils required a system that could not only retrieve information but also provide contextualized insights to support complex decision-making.
Conducting workshops and audits to understand the councils' existing systems, workflows, and pain points.
Developing a tailored cloud architecture using AWS services to address specific challenges and align with the councils' objectives.
Using an agile methodology to deploy the solution in phases, ensuring minimal disruption and seamless integration with existing systems.
Providing extensive training to council staff and developing a change management plan to foster adoption and effective use of the new system.
Offering ongoing support and monitoring to ensure the solution remained effective and adapted to evolving needs.
Working with councils to develop a long-term digital transformation strategy, focusing on future growth and innovation.
Graph Retrieval-Augmented Generation (RAG) System
VE3 developed a Graph RAG system using Amazon Bedrock's Llama3 LLM to break down data silos and provide contextualized insights for decision-making. This system enabled the authority to retrieve relevant information and generate detailed, context-aware responses by leveraging the graph structure in Amazon Neptune to understand data relationships and trends.
Amazon Bedrock for AI Capabilities
VE3 integrated large language models (LLMs) like Llama3 through Amazon Bedrock to enhance the solution's data analysis and decision support capabilities, allowing for more nuanced insights and advanced AI-driven decision-making processes.
Llama3 Large Language Model (LLM)
VE3 customized the Llama3 LLM to effectively process and analyse complex policy and guidance documents. This customization enhanced the system’s ability to provide insightful and accurate decision-making support by understanding the nuanced context of the documents.
Amazon Neptune for Graph Database Management
VE3 used Amazon Neptune to implement an RDF graph-based data model, organizing the authority’s documents stored in Amazon S3 into a structured graph. This approach facilitated efficient storage and querying of interconnected datasets, crucial for understanding data relationships in the RAG system.
Scalable and Flexible AWS Cloud Infrastructure
The solution was built on a scalable and flexible AWS cloud infrastructure using services like Amazon S3, EKS, and AppFlow. The frontend and backend applications, running on Amazon EKS, provided user-friendly interfaces and robust APIs, ensuring that users could easily access and utilize the system’s capabilities.
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VE3’s solution empowered local councils with faster data access, smarter decision-making, scalable infrastructure, and improved operational efficiency, ultimately enabling more effective and responsive public service delivery.
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