AWS Only
Optimising nShift’s Courier Data Retrieval with Generative AI
nShift faced a time-consuming process of manually retrieving courier data from XML files to answer customer queries on speed, cost, and reliability. Firemind built an AI-powered solution leveraging large language models to automate and streamline this process, reducing response times and improving accuracy while integrating seamlessly with nShift’s existing AWS infrastructure.
AWS ONLY ·
Optimising nShift’s Courier Data Retrieval with Generative AI
nShift faced a time-consuming process of manually retrieving courier data from XML files to answer customer queries on speed, cost, and reliability. Firemind built an AI-powered solution leveraging large language models to automate and streamline this process, reducing response times and improving accuracy while integrating seamlessly with nShift’s existing AWS infrastructure.
At a glance
nShift provides a cloud-based platform for e-commerce businesses and logistics providers to manage and optimise shipments. Their platform integrates with multiple carriers, allowing users to select shipping options, automate logistics tasks, and improve delivery efficiency and cost management.
Challenge
nShift's manual process for retrieving courier data from XML files was slow and inefficient, requiring multiple teams to compile information. They needed a solution to automate and speed up data retrieval while maintaining accuracy and improving customer response times.
Solution
Firemind developed an AI-driven solution using large language models to automate nShift's courier data retrieval. By converting XML data to JSON and leveraging AWS infrastructure, the solution significantly reduced response times, improved accuracy, and streamlined query processing.
Services used
Amazon Bedrock
AWS Lambda
Amazon Cognito
AWS CloudFront
Outcomes
Days to minutes with automated data retrieval and response generation using generative AI..
Improved customer experience by providing faster and more accurate courier service recommendations.
Business challenges
Slow, manual courier data retrieval process
nShift struggled with a labour-intensive process for retrieving and processing courier data from XML files. Answering customer queries about courier services required manually sifting through extensive datasets to determine the best options based on speed, cost, hazard, and reliability. This approach was not only time-consuming but also prone to errors, with response times ranging from 30 seconds to up to 5 days.
The inefficiency was exacerbated by the need for multiple teams to collaborate in gathering and cross-referencing data. This cumbersome process hampered nShift’s ability to provide timely and accurate information to their clients, impacting overall customer satisfaction and operational efficiency. The challenge was to find a solution that could automate and expedite data retrieval while maintaining or improving the accuracy of the responses.
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Solution
AI-driven automation for efficient courier data retrieval
To address nShift’s challenge with manual courier data retrieval, Firemind developed an advanced AI-driven solution utilising Amazon Bedrock for large language model capabilities. The project began by converting nShift’s XML data into a more manageable JSON format, which facilitated easier data manipulation and querying. This conversion was essential for integrating the data into a streamlined process that could efficiently handle complex queries.
Firemind’s solution leveraged AWS Lambda and API Gateway to create a serverless architecture, allowing for scalable and rapid processing of user queries. The front-end interface, hosted on AWS S3 and CloudFront, provided a seamless user experience, while AWS Cognito ensured secure authentication. Amazon Bedrock, as the foundation for the large language models, enabled advanced natural language processing to interpret and respond to courier service queries with high accuracy.
The use of Amazon Bedrock allowed the system to automate data retrieval and generate responses to natural language queries effectively. This significantly reduced response times from several days to under a minute and minimised the need for manual intervention. By integrating Bedrock’s AI capabilities, the solution enhanced both the speed and precision of information delivery.
Overall, the implementation of this AI-driven approach streamlined nShift’s operations, improved customer satisfaction with timely and accurate information, and allowed the team to focus on more strategic tasks. The successful use of Amazon Bedrock marked a significant advancement in how nShift manages and delivers courier data.
Accelerated response times
The new AI-driven solution reduced courier data retrieval times from several days to under a minute, dramatically speeding up the process of answering customer queries.
Enhanced accuracy
By leveraging Amazon Bedrock's advanced language models, the system significantly improved the accuracy of responses, ensuring more reliable and precise information for users.
Streamlined operations
The integration of AWS services and AI automation minimised manual intervention, allowing nShift’s team to focus on higher-value tasks and enhancing overall operational efficiency.
Model Spotlight
Claude V2:1
Claude V2:1 was selected for its advanced understanding and contextual capabilities, which are vital for more complex interactions. This model offers enhanced comprehension and nuanced responses, making it perfect for handling intricate tasks and providing in-depth assistance.
Why Firemind
Firemind was chosen as a partner for this project due to their expertise in data, machine learning, and generative AI on AWS. Their proven track record with complex AI solutions and deep knowledge of AWS services made them a natural fit for leveraging advanced technologies to solve nShift’s data retrieval challenges. Additionally, Firemind’s successful collaborations with notable clients and their recognition as a leading AWS partner demonstrated their capability to deliver tailored, innovative solutions that align with nShift’s needs.
100%
Reduction in Manual Data Processing
The automated solution eliminated the need for manual data scraping and analysis, significantly reducing time and effort.
50%
Scalability increase
The AI-driven system enabled Blue Light Card to scale their competitive analysis operations by 50% without adding additional full-time employees (FTEs).
Added value
The AI-driven system provided Blue Light Card with deeper insights and more accurate data, enabling them to make informed, data-driven decisions. With the ability to rapidly process and analyse competitor information, Blue Light Card could quickly adapt to market trends and stay ahead of the competition.
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