RAG-powered AI chatbot handling 50K+ monthly conversations with 1.2s average response time and 96% autonomous resolution rate — reducing support costs by 72%.
Delivered & Live in Production
Live ChatAnalyticsTraining
Live
JR
Hi, I placed an order (#SS-20258174) two days ago and it still shows as "processing." Can you tell me when it'll ship?
Hi Jordan! I've pulled up your order #SS-20258174. It looks like your Ember Pro Wireless Headphones are being packaged at our Austin fulfillment center right now. You'll receive a shipping confirmation with tracking by end of day today, and estimated delivery is Thursday, June 19th. Would you like me to send the tracking link to your email as soon as it's generated?
JR
Yes please! Also — can I add a gift note to the order? It's a birthday present.
Absolutely! I've flagged your order for a gift note. Just type out the message you'd like included and I'll attach it before the package ships. Since it hasn't left the warehouse yet, we can still make that change. 🎁
Active Sessions: 847Avg Response: 1.2sResolution: 96%
Production chat interface — customer data anonymized for display
50K+Monthly Conversations
96%Auto-Resolved
1.2sAvg Response Time
72%Cost Reduction
Before & After
Before Rivan.ai
Response time12 hours
Resolution rate64%
Monthly cost$420K
CSAT score62%
Escalation rate78%
After Rivan.ai
Response time1.2 seconds
Resolution rate96%
Monthly cost$118K
CSAT score94%
Escalation rate4%
The Challenge
ShopStream, an e-commerce company based in Austin, TX, with 4M+ monthly visitors was drowning in support tickets. Their 45-person support team was handling 50K+ tickets monthly with 12-hour average response times. During flash sales and holiday peaks, response times would balloon to 48+ hours. They'd tried three different chatbot solutions — all were keyword-matching bots that frustrated customers and actually increased escalations. CSAT had dropped to 62% and they were hemorrhaging customers to competitors with better support.
The math was brutal: $420K/month in support costs, a 78% escalation rate, and an ever-growing backlog that no amount of hiring could solve. Their VP of Customer Experience had been tasked with cutting costs by 50% while simultaneously improving CSAT — a seemingly impossible mandate that had already outlasted two previous vendors.
Our Solution
We built a RAG-powered AI support agent from scratch, designed to truly understand ShopStream's product catalog, policies, and customer history. The system indexes 15,000+ product pages, 3,200 FAQ articles, and 2 years of successful support transcripts into a vector database, then uses retrieval-augmented generation to craft accurate, contextual responses. What makes it different from the keyword bots they'd tried before is the multi-turn conversation engine — it maintains full context across a conversation, detects customer sentiment in real-time, and knows exactly when to escalate to a human (and does so with full conversation context so the customer never has to repeat themselves).
We also built a self-learning feedback loop where human agent corrections automatically improve the AI's responses. Every time a human agent steps in and resolves a ticket differently than the AI would have, that correction gets fed back into the training pipeline — making the system smarter with every interaction. Within the first month, the AI was handling 96% of all incoming conversations autonomously, and the remaining 4% that reached human agents came with full context so resolution was faster than ever.
RAG Pipeline
Retrieval-Augmented Generation indexing 15K+ knowledge base articles, product docs, and past tickets.
Natural Conversation
Multi-turn dialogue with context retention, sentiment detection, and personality matching.
Smart Escalation
AI-powered routing that detects frustration, complexity, and VIP status for seamless human handoff.
Multilingual
Supports 12 languages with real-time translation and culturally appropriate responses.
Self-Learning
Continuous improvement from human agent corrections and customer feedback loops.
Omnichannel
Deployed across web chat, WhatsApp, Slack, email, and in-app messaging simultaneously.
Tech Stack
Built with cutting-edge AI infrastructure for maximum reliability and performance.
Spent 2 weeks analyzing ShopStream's support operations end-to-end. Mapped all 15K+ knowledge articles, categorized 50K+ historical ticket types by intent and resolution pattern, and identified the top 200 conversation flows that covered 94% of all inquiries.
Feb 2025
RAG Pipeline & Training
Built the vector ingestion pipeline to index 15K+ product pages, 3,200 FAQ articles, and 2 years of support transcripts into Pinecone. Fine-tuned the response generation model and benchmarked it against 10,000 historical tickets to validate accuracy before any live deployment.
Mar — Apr 2025
Core Engine Development
Developed the multi-turn conversation engine with full context retention, real-time sentiment detection, and smart escalation logic. Built omnichannel integration layer for web chat, WhatsApp, Slack, email, and in-app messaging with unified conversation threading.
May 2025
Shadow Testing
Ran the AI alongside human agents for 3 weeks in shadow mode — every ticket was answered by both the AI and a human, and we compared resolution quality side by side. Iterated on 340+ edge cases and tuned the escalation thresholds until the AI matched or exceeded human quality on 96% of conversations.
Jun 2025
Full Deployment
Gradual rollout from 10% to 100% of live traffic over two weeks. Achieved 96% autonomous resolution rate within the first 14 days of full deployment. Activated the self-learning feedback loop and handed over monitoring dashboards and system documentation to the ShopStream team.
Client Feedback
We'd been burned by three chatbot vendors who all promised the world and delivered glorified FAQ search. The Rivan.ai team actually sat with our support agents for two weeks to understand how they solve problems — then built an AI that thinks the same way. Our customers genuinely can't tell the difference between the AI and a human agent. We went from 12-hour response times to 1.2 seconds, and our CSAT jumped 32 points. This single project saved us $3.6M annually in support costs.
MC
Marcus ChenVP of Customer Experience, ShopStream — Austin, TX
Project Verification
U
Upwork ContractFixed-Price Project
Completed
Contract TitleAI-Powered Customer Support System
Client LocationAustin, TX, USA
Budget$84,000
DurationJan — Jun 2025
5.0
"The AI handles 96% of our tickets autonomously. Our support costs dropped 72% in the first month."