Transforming Customer Service with an AI Chatbot for an NDA B2C Commerce Client
An NDA B2C commerce client struggled with high return rates and eroding customer loyalty. Support agents were overwhelmed by repetitive inquiries about orders and returns, leading to inconsistent responses, delays and manual errors. Research shows that a confusing or slow returns process can frustrate customers, while AI chatbots can guide customers through returns, reduce human error and improve satisfaction. Additionally, chatbots provide 24/7 availability and can answer up to 80 % of typical questions, cutting operational costs by up to 30 %.
Project Snapshot
Client profile
Our confidential client operates a B2C e-commerce platform offering a wide range of products. Frequent returns and growing customer-support volumes strained their operations. Customers faced long wait times and received inconsistent information, eroding trust and loyalty.
Project goal
Reduce manual workload, ensure consistent messaging, and be easily embedded into the client’s existing commerce platform. The project also included onboarding customers and creating user instructions for the chat service.
- Embed a responsive chat experience into the web storefront without disrupting the checkout flow
- Ground answers in the client’s knowledge base (policies, FAQs, product content) and live order data
- Automate the majority of repetitive support requests while safely escalating edge cases to humans
- Provide onboarding materials and operational tooling (handover flows, analytics, admin controls)
- Increase ROI by reducing customer service employee workload
Business challenge
Customer support became a bottleneck: response times grew, information quality degraded, and operational costs increased. The client needed a scalable way to deliver accurate, policy-compliant answers—without expanding headcount.
- High returns & strained support team: Support demand grew with returns/changes, creating long queues and slow resolution times—directly impacting customer satisfaction and repeat purchases
- Inconsistent information & human error: Under high load, agents gave mixed guidance about refunds, eligibility, delivery status, and product details—driving complaints and loss of trust
- Cost pressure & scalability: Peak seasons required more agents, but adding headcount didn’t fix consistency issues and created a recurring cost problem
- Low loyalty & retention risk: Customers receiving delayed or conflicting answers were more likely to abandon future purchases, increasing churn and returns-driven losses
Solution
Advantrix Labs designed and deployed an AI-powered customer-service chatbot tailored to the client’s commerce platform. The solution uses retrieval-augmented generation (RAG) to combine the client’s knowledge base and operational data with an LLM—ensuring responses are accurate, consistent, and up to date. Key elements include the following.
- Commerce-native chat UI: Embedded UX seamless widget with guided flows (order lookup, returns initiation, delivery questions) and safe escalation to human support when needed
- RAG orchestration service: A backend service retrieves the right policy/product/order context and generates grounded answers with guardrails and structured response formats
- System integration: Secure and real-time connections to order management and customer data to fetch status, return eligibility, and shipping guidance; ticketing/CRM handoff for escalations
- Operational controls & improvement loop: Conversation logging, quality review, prompt/knowledge updates, and analytics to continuously improve accuracy and deflection
- Failure resistance even on holidays: The chatbot platform was architected for peak-season reliability with autoscaling services, asynchronous processing, retries and circuit breakers, and graceful degradation paths. Comprehensive logging, metrics, and alerting ensured issues were detected and resolved in real time, allowing the system to operate without errors or downtime during holiday traffic spikes and reduced staffing periods
Business outcomes
By integrating a RAG-powered AI chatbot into the commerce platform, Advantrix Labs helped the client transform customer service into a scalable, consistent support channel—reducing cost and improving loyalty.
- Customer retention uplift: Improved support speed and consistency increased customer retention by ~30%
- Support automation & cost reduction: Automated ~70% of customer service workload by resolving routine questions end-to-end, reducing staffing pressure and support costs
- Consistent, error-resistant information: Eliminated misleading guidance by grounding answers in approved policies and product/order context, reducing complaints and unnecessary returns
- Holiday resilience (no downtime incidents): Logs and monitoring confirmed stable performance during holiday spikes—no outages, no degraded answer quality, and no operational backlogs that accompanied customer loyalty augmentation