Fintech

Intelligent Customer Support Automation for FinServ Pro

Buraq built an AI-powered omnichannel support platform that reduced average ticket resolution time by 85% and deflected 60% of inbound queries for a fast-growing fintech serving 2 M+ users.

FinServ Pro

4 months

Primary KPI

85% Faster Resolution

Key measurable outcome

Tech Stack

Next.jsNode.jsAzure FunctionsGPT-4o miniAzure AI SearchMongoDB AtlasWebSocketSendGrid

Problem Statement

FinServ Pro, a digital payments platform with 2.1 million active users, was drowning in customer support volume. The team of 45 agents handled 8,000+ tickets per day across email, in-app chat, and social media — with an average resolution time of 14 hours. Tier-1 queries (password resets, transaction status checks, KYC document uploads) consumed 65% of agent time despite being fully automatable. Churn analysis revealed that 28% of users who contacted support and waited more than 4 hours for a response churned within 30 days. With Series B funding secured and a target to scale to 5 M users, the existing support model was unsustainable both financially and operationally. Previous chatbot implementations by an in-house team achieved only 12% deflection rates due to poor intent recognition and inability to execute transactional actions (e.g., actually resetting a password rather than just linking to a help article).

The Solution

Our Approach

Buraq designed and deployed an AI-first omnichannel support platform combining large-language-model intent classification, retrieval-augmented generation (RAG), and secure transactional automation. Architecture highlights: • Omnichannel Ingestion Gateway — A unified API layer built with Node.js on Azure Functions normalises inbound messages from email (SendGrid webhooks), in-app chat (WebSocket), WhatsApp Business API, and Twitter/X DMs into a common ticket schema stored in MongoDB Atlas. • Intent Classification & RAG Engine — Incoming messages are classified by a fine-tuned GPT-4o mini model into 48 intent categories with 97.3% accuracy. For knowledge-based queries, a RAG pipeline retrieves context from a vector store (Azure AI Search) containing 1,200+ help articles and regulatory FAQ documents, generating accurate, citation-backed responses. • Transactional Action Layer — Unlike typical chatbots, our AI agent executes real actions: password resets via the identity API, transaction-status lookups from the payments microservice, KYC document re-submission triggers, and refund initiations (up to $50, auto-approved by policy). All actions are logged with full audit trails for compliance. • Smart Escalation — When confidence drops below threshold or the query involves disputes over $50, the system seamlessly hands off to a human agent with full conversation context, sentiment analysis, and a suggested resolution. Agents see a co-pilot sidebar with AI-recommended responses they can send with one click. • Analytics Dashboard — A real-time Next.js dashboard tracks deflection rate, CSAT per channel, agent utilisation, and trending issue categories. Automated alerts flag sudden spikes (e.g., a payment-gateway outage causing a ticket surge).

Key Highlights

AI Support

NLP

Fintech

RAG

Omnichannel

Process Automation

The Outcome

Results & Impact

Results within 90 days of launch: • 85% reduction in average resolution time — from 14 hours to 2.1 hours (including human-handled edge cases). • 60% ticket deflection — AI fully resolves 4,800+ tickets per day without human intervention. • 34% reduction in support OpEx — agent headcount held flat at 45 despite 2.5× ticket volume growth. • CSAT score improved from 3.2 to 4.6 out of 5 — users rated AI responses higher than previous human-only responses for Tier-1 issues. • 28% churn-risk segment reduced to 9% — faster resolution directly correlated with improved retention. • Compliance-ready — all AI actions logged with audit trails satisfying PCI-DSS and local financial-regulator requirements. FinServ Pro's VP of Customer Experience noted: "Buraq didn't just give us a chatbot — they gave us an intelligent support system that actually does things. Our agents now focus on complex cases that genuinely need human judgement."

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