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Case Studies/FinTech

AI-Powered Customer Support for FinTech Startup

Client: PayFlow (anonymized)

Challenge

PayFlow was handling 500+ customer support tickets daily with a 4-person team. Average response time was 6 hours, and customer satisfaction was declining. They needed to scale support without proportionally scaling headcount.

Solution

We built an AI-powered support chatbot using Claude with RAG (Retrieval-Augmented Generation) over their knowledge base. The system handles tier-1 queries autonomously and escalates complex issues to human agents with full context.

Results

< 30 seconds
Response Time
73%
Auto-Resolution Rate
4.6/5.0
CSAT Score
60%
Cost Reduction

Background

PayFlow is a Series A fintech startup processing payments for 10,000+ small businesses. Their support volume was growing 20% month-over-month.

The Challenge

  • 500+ tickets/day, growing fast
  • 6-hour average response time
  • 4-person support team at capacity
  • Common questions repeated daily

Our Approach

Week 1: Discovery + Build

  1. Knowledge Base Indexing: Ingested 200+ help articles into a vector database
  2. Intent Classification: Built a classifier to route queries
  3. Response Generation: Claude-powered responses with source citations
  4. Escalation Logic: Confidence-based routing to human agents

Architecture

Customer Message
    ↓
Intent Classifier
    ↓
┌─── High Confidence ──→ AI Response + Sources
│
└─── Low Confidence ───→ Human Agent + AI Context

Results

After 30 days in production:

  • 73% auto-resolution rate (no human needed)
  • < 30 second average response time
  • 4.6/5.0 customer satisfaction
  • 60% cost reduction in support operations

Tech Stack

  • Next.js frontend widget
  • Claude 3.5 Sonnet for generation
  • PostgreSQL + pgvector for RAG
  • Cloudflare Workers for edge deployment