DTDC is India’s main built-in categorical logistics supplier, working the biggest community of buyer entry factors within the nation. DTDC’s technology-driven logistics options cater to a variety of consumers throughout various {industry} verticals, making them a trusted companion in delivering excellence.
DTDC Specific Restricted receives over 400,000 buyer queries every month, starting from monitoring requests to serviceability checks and transport charges. With such a excessive quantity of shipments, their current logistics agent, DIVA, was operated on a inflexible, guided workflow, forcing customers to comply with a structured path fairly than participating in pure, dynamic conversations. The dearth of flexibility resulted in elevated burden on buyer help groups, longer decision occasions, and poor buyer expertise.
DTDC was on the lookout for a extra versatile, clever assistant—one that would perceive context, handle advanced queries, and enhance effectivity whereas decreasing reliance on human brokers. To attain a greater buyer expertise, DTDC determined to reinforce DIVA with generative AI utilizing Amazon Bedrock.
ShellKode is an AWS Associate, born-in-the-cloud firm specializing in modernization, safety, knowledge, generative AI, and machine studying (ML). With a mission to drive transformative development, ShellKode empowers companies by means of state-of-the-art know-how options that tackle advanced challenges and unlock new alternatives. Utilizing deep {industry} experience, they ship tailor-made methods that foster innovation, effectivity, and long-term success in an evolving digital panorama.
On this submit, we talk about how DTDC and ShellKode used Amazon Bedrock to construct DIVA 2.0, a generative AI-powered logistics agent.
Resolution overview
To handle the constraints of the present logistics agent, ShellKode constructed a sophisticated agentic assistant utilizing Amazon Bedrock Brokers, Amazon Bedrock Data Bases, and an API integration layer.
When prospects work together with DIVA 2.0, they expertise a seamless, conversational interface that understands and responds to their queries naturally. Whether or not monitoring a bundle, checking transport charges, or inquiring about service availability, customers can ask questions in their very own phrases with out following a inflexible script. DIVA 2.0’s enhanced AI capabilities permit it to know context, handle advanced requests, and supply correct, personalised responses, considerably bettering the general buyer expertise and decreasing the necessity for human intervention. The next high-level structure diagram illustrates the applying circulation and the answer structure with AWS companies.
The DTDC logistics agent is designed utilizing a modular and scalable structure to offer seamless integration and excessive efficiency. This streamlined workflow demonstrates how a generative AI-powered serverless logistics agent utilizing AWS App Runner, Amazon Bedrock Brokers, AWS Lambda, and a vector-based information base handles person queries starting from monitoring requests to serviceability checks and transport charges intelligently and effectively.
The logistics agent is hosted as a static web site utilizing Amazon CloudFront and Amazon Easy Storage Service (Amazon S3). The logistics agent is built-in with the DTDC website, which gives an intuitive and user-friendly interface for end-user interactions (see the next screenshot).
An end-user accesses the logistics agent by means of the DTDC web site and submits queries like monitoring shipments, checking service availability, calculating transport charges, FAQs, and so forth utilizing pure language.The person requests are processed by App Runner, which helps run the net utility (together with API companies, backend internet companies, and web sites) on AWS. App Runner is hosted with a number of API companies, such because the Amazon Bedrock Brokers API and Dashboard API. App Runner initiates the Amazon Bedrock Brokers API primarily based on the person requests.
Amazon Bedrock is a completely managed service that provides a alternative of {industry} main basis fashions (FMs) together with a broad set of capabilities to construct generative AI functions, simplifying improvement with safety, privateness, and accountable AI. With Amazon Bedrock, your content material will not be used to enhance the bottom fashions and isn’t shared with any mannequin suppliers. Amazon Bedrock Guardrails gives configurable safeguards to assist safely construct generative AI functions at scale. To be taught extra, see Construct protected and accountable generative AI functions with guardrails. AWS Id and Entry Administration (IAM) helps directors securely management who could be authenticated and approved to make use of Amazon Bedrock sources.
The Amazon Bedrock brokers are configured in Amazon Bedrock. An Amazon Bedrock agent receives the request and interprets the person’s intent utilizing its pure language understanding capabilities. Based mostly on the interpreted intent, the agent triggers an applicable Lambda perform, comparable to:
- Monitoring consignments
- Pricing info
- Location serviceability examine
- Help ticket creation
The triggered Lambda perform calls the next consumer APIs, retrieves the related knowledge, and returns the response to the agent:
- Monitoring System API – Retrieves real-time standing and gives updates on consignment cargo monitoring
- Supply Franchise Location API – Checks the service availability to ship the parcels between the areas
- Pricing System API – Calculates the transport charges primarily based on cargo particulars supplied by the person
- Buyer Care API – Creates a help ticket for the end-users
The agent passes the response to the big language mannequin (LLM), on this case Anthropic’s Claude 3.0 on Amazon Bedrock, which understands the context of the retrieved knowledge, processes it, and generates a significant response for the person.
The information base accommodates web-scraped content material from the DTDC web site, inner help documentation, FAQs, and operational knowledge, enabling real-time updates and correct responses. The information base contents are saved as vector embeddings in Amazon OpenSearch Service, offering fast and related responses. For common queries, the logistics agent fetches info from Amazon Bedrock Data Bases, offering accuracy and relevance. Utilizing semantic similarity search, related chunks of data are retrieved from the information base primarily based on the person’s question, which Amazon Bedrock then makes use of to generate a context-aware response. If no related knowledge is discovered within the information base, a fallback response (preconfigured within the Amazon Bedrock immediate) is returned, indicating that the system couldn’t help with the request.
The logistics agent queries and related responses are saved in Amazon Relational Database Service (Amazon RDS) for PostgreSQL for enhanced scalability and relational knowledge dealing with. App Runners initiates the Dashboard API name to replace the queries and related responses in Amazon RDS. We talk about this in additional element the next part.
All through the method, Amazon CloudWatch Logs captures key occasions comparable to intent recognition, Lambda invocations, API responses, and fallback triggers for auditing and system monitoring. AWS CloudTrail data and displays exercise within the AWS account, together with actions taken by customers, roles, or AWS companies. It logs these occasions, which can be utilized for operational auditing, governance, and compliance.
Amazon GuardDuty is a menace detection service that repeatedly displays, analyzes, and processes AWS knowledge sources and logs in your AWS atmosphere. GuardDuty makes use of menace intelligence feeds, comparable to lists of malicious IP addresses and domains, file hashes, and ML fashions to establish suspicious and probably malicious exercise within the AWS atmosphere.
Logistics agent dashboard
The next high-level structure diagram illustrates the logistics agent dashboard, which captures the end-user interactions and its related responses.
The logistics agent dashboard is hosted as a static web site utilizing CloudFront and Amazon S3. Dashboard entry is allowed just for the DTDC admin group.
The dashboard is populated by means of API calls utilizing Amazon API Gateway with Lambda as a backend, which retrieves the dashboard knowledge from Amazon RDS for PostgreSQL.
The dashboard gives real-time insights into the logistics agent efficiency, together with accuracy, unresolved queries, question classes, session statistics, and person interplay knowledge (see the next screenshot). It gives actionable insights with options comparable to warmth maps, pie charts, and session logs. Actual-time knowledge is logged and analyzed on the dashboard, enabling steady enchancment and fast situation decision.
Resolution challenges and advantages
When implementing DIVA 2.0, DTDC and ShellKode confronted a number of vital challenges. Integrating real-time knowledge from a number of legacy programs was essential for offering correct, up-to-date info on monitoring, charges, and serviceability. This was seemingly addressed by means of the strong API integration capabilities of Amazon Bedrock Brokers. One other main hurdle was coaching the AI to know advanced logistics terminology and multi-step queries, which was overcome by utilizing Amazon Bedrock LLMs and Amazon Bedrock Data Bases, fine-tuned with industry-specific knowledge. The group additionally needed to navigate the fragile means of transitioning from the outdated inflexible DIVA system whereas sustaining service continuity and preserving historic knowledge, probably using a phased method with parallel programs. Lastly, scaling the answer to deal with over 400,000 month-to-month queries whereas sustaining efficiency was a major problem, addressed by utilizing the cloud infrastructure of Amazon Bedrock Brokers for optimum scalability and efficiency. These challenges underscore the complexity of upgrading to an AI-powered system in a high-volume, data-intensive {industry} like logistics, and spotlight how AWS options supplied the required instruments to beat these obstacles. DTDC realized the next advantages from powering the logistics agent with generative AI utilizing Amazon Bedrock:
- Enhanced conversations and real-time knowledge entry with buyer help brokers – Powered by Amazon Bedrock Brokers, the answer improves pure language understanding, enabling extra fluid and fascinating conversations. With multi-step reasoning, it could deal with a broader vary of queries with larger accuracy. Moreover, by integrating seamlessly with DTDC’s API layer, the logistics agent gives real-time entry to important info, comparable to monitoring shipments, service availability, and calculating transport charges. The mixture of superior conversational capabilities and real-time knowledge gives quick, correct, and contextually related responses.
- Clever knowledge processing and correct FAQ responses – For advanced queries, the logistics agent makes use of LLM know-how to course of uncooked knowledge and ship structured, tailor-made responses. This makes certain customers get clear, actionable insights. For often requested questions, the logistics agent makes use of Amazon Bedrock Data Bases to ship exact solutions with out requiring human help, decreasing wait occasions and enhancing the general person expertise.
- Diminished dwell agent dependency and steady enchancment – Though the logistics agent hasn’t eradicated the necessity for buyer help, the variety of queries dealt with by the shopper help group has lowered by 51.4%. The system gives beneficial insights into key efficiency metrics like peak question occasions, unresolved points, and total engagement by means of built-in real-time analytics, serving to refine and enhance the assistant’s capabilities over time.
Outcomes
The generative AI-powered logistics agent has lowered the burden on buyer help groups and shortened decision occasions, leading to higher buyer expertise:
- Powered by Amazon Bedrock, DIVA 2.0 understands queries in pure language and helps dynamic conversations with a response accuracy of 93%
- Based mostly on the final 3 months of dashboard metrics knowledge, they noticed the next:
- 71% of the inquiries had been associated to consignments (256,048), whereas 29.5% had been common inquiries (107,132)
- 51.4% of consignment inquiries (131,530) didn’t end in a help ticket, whereas 48.6% (124,518) led to new help ticket creation
- Of the inquiries that resulted in tickets, 40% began with the shopper help heart earlier than transferring to the AI assistant, whereas 60% started with the assistant earlier than involving the shopper help heart
DIVA 2.0 has lowered the variety of queries dealt with by the shopper help group by 51.4%. DTDC’s help group can now concentrate on extra vital points, bettering total effectivity.
Abstract
This submit demonstrated how Amazon Bedrock can rework a standard chatbot to a generative AI-powered logistics agent that gives higher buyer expertise by means of dynamic dialog. For companies going through related challenges, this resolution affords a blueprint for modernizing your AI assistant whereas sustaining compliance with {industry} requirements.
To be taught extra about this AWS resolution, contact AWS for additional help. AWS can present detailed details about implementation, pricing, and methods to tailor the answer to your particular enterprise wants.
In regards to the authors
Rishi Sareen – Chief Info Officer (CIO), DTDC is a seasoned know-how chief with over 20 years of expertise in driving digital transformation, enterprise IT technique, and innovation throughout the logistics and provide chain sector. He focuses on constructing agile, AI-driven, and safe know-how ecosystems that improve operational effectivity and buyer expertise. Rishi leads initiatives spanning system modernization, knowledge intelligence, automation, cybersecurity, cloud, and synthetic intelligence. He’s deeply dedicated to aligning know-how with enterprise outcomes whereas fostering a tradition of steady enchancment and purposeful innovation. A robust advocate for people-centric management, Rishi locations excessive emphasis on nurturing expertise, constructing high-performing groups, and mentoring future-ready know-how leaders who can thrive in dynamic, AI-powered environments. Recognized for his strategic imaginative and prescient and disciplined execution, he has led large-scale digital initiatives and transformation packages that ship lasting enterprise influence.
Arunraja Karthick – Head – IT Providers & Safety (CISO), DTDC is a strategic IT and cybersecurity chief with over 15 years of expertise driving enterprise-scale digital transformation. Because the Head of IT Providers & Safety (CISO) at DTDC Specific Restricted, he leads the group’s core IT, cloud, and safety packages—remodeling legacy environments into agile, safe, and cloud-native ecosystems. Below his management, DTDC has adopted a hybrid cloud structure spanning AWS, GCP, and on-prem colocation, with a imaginative and prescient to allow dynamic workload mobility and vendor-neutral scalability. Arunraja has led vital modernization efforts, together with the migration of key enterprise functions to microservices and containerized platforms, whereas guaranteeing excessive availability and regulatory compliance. Recognized for his deep technical perception and execution self-discipline, he has applied enterprise-wide cybersecurity frameworks—from E mail DLP, Cell System Administration, and Conditional Entry to Hybrid WAF and superior SOC operations. He has additionally championed safe entry transformation by means of Zero Belief-aligned Safe WebVPN, redefining how inner customers entry company apps. Arunraja’s management is grounded in platform considering, automation, and a user-first mindset. His latest initiatives embrace the enterprise rollout of GenAI copilots for buyer expertise and operations, in addition to unified policy-based DLP and content material management mechanisms throughout endpoints and cloud. Acknowledged as an Influential Know-how Chief, Arunraja continues to problem standard IT boundaries—aligning safety, agility, and innovation to energy enterprise evolution.
Bakrudeen Ok an AWS Ambassador, leads the AI/ML observe at Shellkode, specializing in driving innovation in synthetic intelligence, particularly in Generative AI. He performs a key function in constructing groups and superior AI options, Agentic Assistants, and different next-gen applied sciences. Bakrudeen has made notable contributions to AI/ML analysis and improvement. In 2023 and 2024, he obtained the Generative AI Consulting Excellence Associate Award on the AI Conclave and the Social Affect Associate of the Yr Award for Generative AI at AWS re:Invent 2024, each on behalf of Shellkode reflecting the group’s sturdy dedication to innovation and influence within the AI area.
Suresh Kanniappan is a Options Architect at AWS, dealing with Automotive, Manufacturing and Logistics enterprises in India. He’s enthusiastic about cloud safety and Business options that may clear up actual world issues. Previous to AWS, he labored for AWS prospects and companions in consulting, migration and resolution structure roles for over 14 years.
Sid Chandilya is a Sr. Buyer Relations Supervisor at AWS, liable for tech led enterprise transformation with Automotive, Manufacturing and Logistics enterprises in India. Sid is peculiarly enthusiastic about difficult standing quos, constructing a joint “Assume Large” imaginative and prescient with buyer CXOs and leveraging Ai infused tech to speed up outcomes. He’s recognized for his deep understanding of {industry} imperatives (working backward from buyer) and translating the enterprise ache factors into tech resolution.
