About
Foxintelligence, part of NielsenIQ, provides privacy-first market and shopper insights to help global brands and retailers enhance digital performance. The company specialises in transforming unstructured consumer data into valuable, actionable intelligence.
Challenge
Foxintelligence relied heavily on manual processes to process and extract insights from unstructured customer data, such as email communications. This approach was time-consuming, resource-intensive, and limited the company’s ability to scale operations without hiring additional staff.
To remain competitive, Foxintelligence needed a solution that could automate email data extraction, improve processing accuracy, and handle increasing data volumes while maintaining strict privacy and security standards. The goal was to free up its team to focus on higher-value, strategic tasks rather than repetitive data handling.
A proof-of-concept large language model (LLM) solution was developed using AWS services to automate and scale unstructured data processing. Amazon S3 was used to securely ingest and store customer emails, while Amazon Kendra enabled intelligent search and retrieval of the most relevant documents. Amazon Bedrock hosted Claude V2, which provided contextual reasoning and accurate extraction of key information.
The system used a fully serverless architecture with AWS Lambda and Amazon API Gateway for backend processing, ensuring scalability and cost efficiency. A secure, prompt-based user interface allowed Foxintelligence’s team to easily interact with the model, retrieve insights, and customise queries - streamlining workflows and dramatically increasing productivity.
Services used
- Amazon Bedrock
- Amazon Kendra
- AWS Lambda
- Amazon API Gateway
Results
Automated processing of unstructured customer email data
Scalable solution capable of replacing 6,000 manual parsers
Improved accuracy of extracted data using LLMs
Increased productivity and reduced dependency on manual labour
Flexible, serverless architecture for cost-efficient scaling
The team was highly reactive, promptly addressing our needs and providing valuable insights that helped us advance in our R&D process. Their innovative approach and proactive suggestions made a significant impact on the project’s success.
