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Agentic AI

Autonomous Customer Support Agent Resolves 74% of Tickets Without Human Intervention

TradePulseB2B SaaS / FintechMumbai, IndiaDelivered in 5 weeks2 engineers

The Challenge

TradePulse's support team of 8 was handling 1,200+ tickets per month. Average response time was 6 hours. Customer churn due to slow support was measurable, and scaling the team was not viable at their burn rate.

Our Solution

We built a three-layer agentic system: a Classifier Agent that triages incoming tickets by intent and urgency, a Resolver Agent that queries their live database (account status, transaction history, billing) and handles standard requests autonomously, and an Escalation Agent that prepares a full context brief for the human agent when a ticket needs human eyes. The entire system integrates with Intercom via webhooks.

The Results

74%

Tickets resolved without human touch

4 min

Average first-response time (was 6 hrs)

₹18L

Annual support cost saved

4.8★

CSAT score (up from 3.6)

Our support costs dropped by over 18 lakhs in the first year. The agent handles things our human team used to spend hours on — instantly, correctly, every time.

RM

Rajan Mehta

Founder & CEO, TradePulse

Tech Stack

GPT-4oLangGraphFastAPIPostgreSQLIntercom APIRedis

Project Details

ClientTradePulse
IndustryB2B SaaS / Fintech
LocationMumbai, India
Timeline5 weeks
Team2 engineers

Key Outcomes

  • 74%Tickets resolved without human touch
  • 4 minAverage first-response time (was 6 hrs)
  • ₹18LAnnual support cost saved
  • 4.8★CSAT score (up from 3.6)
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