Retail Chatbot Automation Case Study: From DIY Confusion to AI Clarity
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Project Highlights
Company, one of the UK’s leading home improvement retailers, decided to improve their customer experience by building an AI-powered virtual assistant. The company wanted to reduce pressure on support teams while giving customers fast and reliable answers. SLM Software’s team contributed as backend specialists, designing and integrating the intelligent serverless infrastructure that powers Company’ virtual assistant. We ensured the solution was scalable, secure, and capable of delivering human-like, real-time responses.
The Challenge
Customer service for large retailers is complex. People frequently reach out for help with order tracking and delivery questions, or try to check product availability and receive purchasing support. On top of that, companies usually have regular customer satisfaction surveys and offer customers escalation to live agents when it comes to complex issues.
Manual systems could not scale effectively, especially during seasonal demand spikes. Wait times increased, support teams were overloaded, and costs climbed. Company needed a system that could handle the majority of routine queries autonomously while providing smooth handoff to human agents when necessary.
Our Approach
SLM Software engineers designed and deployed backend services that connected Company’ customer-facing chatbot to AWS AI services. We focused on:
Serverless Backend Architecture
Built and deployed AWS Lambda functions with Amazon API Gateway to orchestrate customer workflows (order queries, product searches, and feedback collection).
Natural Language Understanding
Integrated AWS Lex to interpret customer intent and trigger the right workflow, making the assistant conversational and context-aware.
Generative AI Responses
Used Amazon Bedrock for dynamic, flexible, and human-like responses that enhanced engagement.
Human Escalation
Designed fallback logic for seamless escalation to live support agents when customer needs exceeded automation.
Continuous Monitoring & Optimization
Implemented CloudWatch and Sentry for performance monitoring, real-time error detection, and iterative improvements.
The Impact
The Company Virtual Assistant quickly became a valuable support tool, handling thousands of daily interactions with speed and accuracy.
- Customer wait time was reduced by 70%.
- 85% of queries are now able to be handled autonomously
- The Virtual Assistant deflected 40% of requests from human agents, lowering operational costs.
- Customer satisfaction scores grew by 25%.
We Covered
- Serverless backend with AWS Lambda & API Gateway
- NLP integration via AWS Lex for intent recognition
- Generative AI with Amazon Bedrock for natural responses
- Smart escalation flows to human agents
- Monitoring and optimization with CloudWatch & Sentry
AI-Powered Outcomes
Leveraging AWS Lex, Bedrock, Connect & OpenAI for Scalable User Support
Ensure the AI assistant has the most accurate and current information by synchronizing knowledge from Zendesk and internal sources into AWS Bedrock regularly.
What We Learned
Backend AI integration is all about shaping customer experience: automation when possible, humans when necessary. By deflecting routine queries and automating workflows, Company significantly improved customer experience while enabling support staff to focus on higher-value interactions. Moreover, with AWS AI tools, automation doesn’t have to feel robotic; it can feel personal
Ready to Automate Customer Experience?
At SLM Software, we specialize in building intelligent, scalable AI solutions that combine backend reliability with customer-focused design. Contact us to talk about your AI automation project!
