Meet FinBot
Interact with the AI support agent that resolves tier-1 tickets autonomously, password resets, transaction disputes, KYC questions, and escalates complex cases with full context.
Instant Resolution
AI classifies intent, retrieves context from knowledge base, and resolves tier-1 tickets in under 45 seconds
Fraud Detection
Real-time transaction pattern analysis flags suspicious activity and triggers instant card freeze with customer notification
Smart Escalation
Confidence scoring routes complex cases to the right specialist with full conversation context, no customer repeating
Learning Engine
Continuously improves from resolution data, every resolved ticket makes the next one faster and more accurate
Build Your Own FinBot
Configure the knowledge base, select the AI backbone, and define escalation rules.
Customize FinBot
Train the AI on your products, set the LLM backbone, tune response parameters, and define the personality for every customer interaction.
Client Dashboard
A unified portal where clients manage accounts, track investments, view transaction history, and chat with FinBot, all in one place.
Specialist Panel
Review escalated cases, authorize high-value transactions, manage compliance flags, and override AI decisions with full audit trail.
Support Command Center
Real-time ticket queue, resolution metrics, and agent performance, all in one view.
Hi! I'm FinBot, NovaPay's AI financial assistant. I can help with account management, investments, stocks, crypto, payment plans, budgeting, and support issues. How can I help you today?
Operations Overview
The Challenge
NovaPay was scaling fast but their 4-person support team was drowning in repetitive tickets - password resets, transaction disputes, KYC questions. Average response time had climbed to 14 hours, and CSAT dropped to 62%. They needed to handle 3x the volume without tripling headcount.
The Approach
I started with a 2-week discovery phase, analyzing 6 months of ticket data to classify inquiry types and identify automation candidates. Then I designed a multi-stage AI pipeline: intent classification, context retrieval from their knowledge base using vector embeddings, response generation with Claude API, and a confidence-scoring system that routes low-confidence responses to humans. Built the backend in Python with FastAPI, integrated with their existing Zendesk via webhooks, and deployed on AWS with auto-scaling.