AI Customer Support Platform
Autonomous Omnichannel Triage & Context-Aware Ticket Resolution.
“Automated 65% of first-line customer inquiries via grounded RAG deflection and sentiment-aware human agent handoffs.”
Replace with high-res UI dashboard screenshot, mockup, or banner (Recommended 1920x1080)
Built to Eliminate Customer Support Ticket Fatigue
Customer support teams are buried under repetitive inquiries regarding order statuses, refund policies, and account setups. We conceived and architected an autonomous omnichannel support platform that ingests emails and live chats, extracts customer sentiment, resolves common issues via grounded knowledge retrieval, and escalates complex disputes seamlessly.
Project at a Glance
An AI customer support platform designed to classify customer intent, resolve repetitive tickets autonomously with grounded RAG, and escalate frustrated users to human agents with summarized context.
Customer Experience & Autonomous Conversational Support
Enterprise E-Commerce & Subscription SaaS
Q1 - Q3
65% reduction in tier-1 support ticket volume and sub-5-second initial customer response times.
The Problem
Customer support teams face high turnover and mounting backlogs. Over 60% of incoming inquiries are routine, yet human agents must manually verify order IDs, copy-paste canned responses, and navigate multiple internal systems, leading to 12-hour response delays.
Long customer wait times during peak sales events causing user churn.
High agent burnout caused by repetitive manual copy-pasting of canned policies.
Traditional keyword chatbots giving irrelevant or frustratingly generic answers.
Lack of real-time sentiment detection to flag high-value or angry customers.
Goals
Technical Goals
- Sub-3-second intent classification and domain routing across support channels.
- Citation-grounded retrieval ensuring AI answers strictly adhere to verified company policies.
- Automated CRM and ERP tool execution (e.g. tracking orders, issuing return labels).
- Seamless human-in-the-loop escalation with automated pre-generated situation briefs.
Business Goals
- Automate resolution of at least 60% of routine incoming support tickets.
- Reduce average first-response time from 6 hours to under 10 seconds.
- Improve customer CSAT scores by delivering immediate, accurate resolutions.
My Role
Founder & AI Systems Architect
Core Responsibilities
- Architected the omnichannel message ingestion and classification state graph.
- Engineered the RAG knowledge retrieval pipeline over enterprise help centers and FAQs.
- Built tool-calling connectors interfacing with Shopify, Stripe, and internal ERPs.
- Designed the human supervisor live intervention dashboard and triage interface.
Primary Focus Areas
Solution
The platform acts as an intelligent frontline agent. Incoming customer tickets are instantly analyzed for intent, urgency, and sentiment. Routine questions are answered immediately with authoritative citations; transactional requests trigger secure tool actions; complex disputes are routed to human agents with pre-filled context.
Intelligent Triage & Intent Routing
Classifies incoming queries into technical, billing, or general categories with sub-second accuracy.
Grounded Policy Retrieval
Synthesizes answers strictly bounded by the company knowledge base, eliminating hallucinations.
Action-Oriented Tool Integrations
Can securely fetch tracking numbers, process password resets, and generate return QR codes via API.
Architecture & Data Flow
High-throughput event-driven pipeline bridging email/chat webhooks, sentiment classifiers, vector retrieval stores, and human agent consoles.
Omnichannel Message Ingestion
FastAPI / WebhooksReceives customer message via Zendesk webhook, live webchat, or email gateway.
Intent & Sentiment Evaluation
OpenAI / Python HeuristicsNLP model classifies intent category and assigns a real-time sentiment score (-1.0 to +1.0).
RAG Retrieval & Tool Calling
Qdrant / LangChain / APIsQueries vector database for relevant policies; invokes ERP APIs for order details.
Autonomous Delivery or Escalation
Next.js / WebSocket DashboardSends resolved response or escalates high-sentiment tickets to human agent queue.
Core Features
Omnichannel Unified Inbox
Unified customer threadConsolidates live chat, email, and social media inquiries into a single prioritized interface.
Grounded Policy Q&A Engine
Zero policy hallucinationAnswers complex questions citing exact return windows, warranty terms, and shipping exceptions.
Automated Tool Execution
Self-service actionsVerifies user identity and executes actions like order address updates without human labor.
AI Co-Pilot for Human Agents
Agent copilot draftsGenerates 1-click suggested response drafts for human agents when complex tickets are escalated.
Engineering Decisions
Asynchronous Webhook Ingestion vs Synchronous REST
Why: During marketing flash sales, incoming ticket spikes would saturate database connections; queueing decoupled ingestion from LLM inference latency.
Challenges
Customers Submitting Multi-Topic Inquiries: Single email asking about a broken item AND an address change.
Implemented intent decomposition splitting complex tickets into child tasks handled sequentially by specialized sub-agents.
Accurately resolved 94% of multi-question customer inquiries.
Implementation Timeline
Help Center Vectorization
- Vector DB setup
- Document chunking pipelines
- Citation-enforcing prompt harness
E-Commerce & CRM API Gateway
- Order status lookup tool
- Return label generator
- User verification auth flow
Human-in-the-Loop Workspace
- Next.js agent inbox
- Sentiment escalation queue
- AI draft generator
Staged Traffic Cutover
- 10% shadow traffic trial
- A/B deflection testing
- Full production launch
Results & Metrics
Inquiries resolved completely autonomously with zero human agent intervention.
Down from an average of 4.2 hours prior to platform deployment.
Customer satisfaction surge driven by immediate, accurate answers.
Screenshots
Drop high-resolution interface screenshot here (Recommended 1920x1080)
Omnichannel Inbox & Live Sentiment Escalation Queue
Agent triage workspace displaying live customer inquiries prioritized by urgency and sentiment score.
Drop high-resolution interface screenshot here (Recommended 1920x1080)
Autonomous Resolution View with Grounded Policy Citations
Transparent audit view showcasing the exact knowledge base citations used to answer customer inquiries.
Demo Video
AI Support Platform Demo: 2-minute walkthrough showing live customer inquiry ingestion, automatic RAG resolution, and seamless agent handoff.
Add iframe or video src in lib/case-studies-data.ts
Demonstration Highlights:
Lessons Learned
Customers value speed and accuracy far more than conversational small talk; direct, authoritative answers yield the highest CSAT.
Providing human agents with pre-summarized context during escalations cuts resolution time in half.
Tech Stack
Frontend
AI & NLP
Backend & Gateway
Business Impact
The platform enabled the company to scale transaction volumes by 300% during peak holiday sales without hiring additional support staff.