Boosting Customer Engagement and Efficiency: AI Chatbot Rollout for a Leading Tile Distributor

Boosting Customer Engagement and Efficiency : AI Chatbot Rollout for a Leading Tile Distributor

Challenges

1- Manual chatbot responses.

2- No automated escalation flow.

3- Fragmented data storage.

4- No real-time monitoring.

5- Scattered product content.

Solutions

1- RAG-powered responses.

2- Automated escalation emails.

3- Centralized Supabase storage.

4- FastAPI Admin Dashboard.

5- Website content scraper with Astra DB.

Results

1- Context-aware AI responses.

2- Automated escalation handling.

3- Real-time monitoring and reporting.

4- Instant CSV exports.

5- Reliable, domain-specific knowledge base.

The client, a tile and stone company, struggled to manage customer queries on product details, lead times, and issue reporting. Without a centralized system, responses were inconsistent, escalations were delayed, and there was no structured repository for conversation history.

They needed an intelligent chatbot that could provide accurate, context-based responses, automate escalations, and maintain a centralized record of all conversations. Real-time monitoring, analytics, and export capabilities were also essential.

An AI chatbot with RAG for contextual replies, automated escalations via Microsoft Graph API, centralized storage in Supabase, a FastAPI dashboard for monitoring/exports, and Astra DB for structured product knowledge.

Key Industry

Ceramic Tile and Flooring Distribution

Key Pains

- Lack of context-aware automated chatbot responses.

- Inability to automatically escalate complex queries.

- No centralized repository of conversation history.

- No real-time visibility into chatbot interactions.

- Scattered product and support content unavailable for retrieval.

Product Mix

- Azure OpenAI

- Groq

- Astra DB (Cassandra)

- Supabase

- Microsoft Graph API

- FastAPI

The outcome
  • Inconsistent and Incomplete AI Responses: Chatbot answers often missed context or failed to resolve complex queries.
  • Manual Escalation Workflow: Escalations were fragmented and time-consuming without automation.
  • No Monitoring or Export Mechanism: Managers lacked real-time oversight of chatbot interactions.
  • Lack of Audit-Ready Records: Conversation history wasn’t stored in a structured, retrievable format.
  • Scattered Product Information: Critical details were spread across the website, limiting chatbot accuracy.
  • Context-Aware Retrieval-Augmented Generation - RAG-based retrieval ensured contextual, accurate responses.
  • Automated Escalation Flow - Microsoft Graph API automatically generated and sent escalation emails.
  • Centralized Conversation Storage - Supabase stored structured conversations by session and timestamp.
  • Admin Dashboard for Monitoring & Management - Admin Dashboard enabled monitoring, record filtering, and quick CSV exports for management use.
  • Website Content Scraping & Storage - Product and policy data scraped and stored in Astra DB for chatbot retrieval.

95%

Instant, Contextual Chatbot Responses

The chatbot now delivers precise, context-aware answers in real time, minimizing repetitive queries and improving user engagement.

85%

Faster Escalation Handling

The automated escalation flow significantly reduced response time for complex cases, ensuring quicker resolutions and improved client satisfaction.

95%

Improved Data Accessibility

Centralized chat records enhanced compliance and visibility, with most conversations now stored in a structured and easily retrievable format.

88%

Expanded Knowledge Accuracy

Structuring website content in Astra DB increased the chatbot’s knowledge coverage, delivering more relevant responses and elevating customer satisfaction.

80%

Enhanced Reporting Efficiency

Managers can now generate and export reports within seconds, improving audit readiness and analysis efficiency.

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