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HomeApplications & Use CasesDTDC Revolutionizes Logistics with Generative AI: Introducing DIVA 2.0...

DTDC Revolutionizes Logistics with Generative AI: Introducing DIVA 2.0 Powered by Amazon Bedrock

TLDR: DTDC Express Limited, a leading Indian logistics provider, has significantly upgraded its customer service capabilities by enhancing its existing DIVA logistics agent with generative AI, leveraging Amazon Bedrock. Developed in collaboration with AWS Partner ShellKode, the new DIVA 2.0 offers a flexible, conversational interface, understanding natural language queries with 93% accuracy, thereby reducing the burden on human support teams and improving customer experience.

New Delhi, India – August 7, 2025 – DTDC Express Limited, India’s foremost integrated express logistics provider, has announced a major advancement in its customer service operations with the launch of DIVA 2.0, a generative AI-powered logistics agent. This enhancement, built on Amazon Bedrock in collaboration with AWS Partner ShellKode, aims to transform customer interactions by providing a more intuitive and efficient support experience.

DTDC, which manages over 400,000 customer queries monthly, previously relied on an older version of DIVA that operated on a rigid, guided workflow. This lack of flexibility often forced users into structured paths, leading to increased strain on customer support teams, longer resolution times, and a less-than-optimal customer experience. Recognizing the need for a more intelligent and adaptable assistant, DTDC sought a solution that could understand context, manage complex queries, and enhance overall efficiency while reducing reliance on human agents.

The solution came through a strategic partnership with ShellKode, an AWS Partner specializing in modernization, security, data, generative AI, and machine learning. Together, they leveraged Amazon Bedrock to develop DIVA 2.0. The new agent provides a seamless, conversational interface, allowing customers to ask questions in natural language without adhering to a rigid script. Whether tracking a package, checking shipping rates, or inquiring about service availability, DIVA 2.0 is designed to understand and respond dynamically.

Key metrics from the first three months of operation highlight the success of DIVA 2.0. Data indicates that 71% of inquiries (256,048) were related to consignments, while 29.5% (107,132) were general inquiries. Crucially, 51.4% of consignment inquiries (131,530) were resolved by DIVA 2.0 without the need for a support ticket, significantly reducing the workload on human agents. The generative AI-powered agent boasts a response accuracy of 93%.

“The generative AI-powered logistics agent has reduced the burden on customer support teams and shortened resolution times, resulting in better customer experience,” stated a representative involved in the project. The architecture of DIVA 2.0 is modular and scalable, utilizing Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and an API integration layer. The streamlined workflow incorporates AWS App Runner, AWS Lambda, and a vector-based knowledge base to intelligently and efficiently handle diverse user queries. The logistics agent is hosted as a static website using Amazon CloudFront and Amazon Simple Storage Service (Amazon S3), ensuring high performance and seamless integration.

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This initiative underscores DTDC’s commitment to leveraging cutting-edge AI technology to enhance operational excellence and deliver superior customer service in the competitive logistics sector.

Ananya Rao
Ananya Raohttps://blogs.edgentiq.com
Ananya Rao is a tech journalist with a passion for dissecting the fast-moving world of Generative AI. With a background in computer science and a sharp editorial eye, she connects the dots between policy, innovation, and business. Ananya excels in real-time reporting and specializes in uncovering how startups and enterprises in India are navigating the GenAI boom. She brings urgency and clarity to every breaking news piece she writes. You can reach her out at: [email protected]

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