LatinAmericanCupid — AI Replaces
2,000-Person Support Team
Client’s Challenge
LatinAmericanCupid is one of Latin America’s largest dating platforms with millions of active users. Their support team numbered 2,000 agents across 5 countries, handling predominantly repetitive inquiries: payment issues, subscription questions, and app technical problems.
The problem: support costs exceeded $6M annually and grew faster than revenue. CSAT remained at 87% — below competitor benchmarks. Agents experienced burnout from monotonous work with 40% annual turnover. Average response time: 4-8 hours.
Our solution: a fully autonomous multi-agent AI system with intelligent human escalation for edge cases.
- $6M+ annual support cost across 5 countries
- 87% CSAT — below industry benchmark of 91%
- 4-8 hour average response time
- 73% of tickets: identical billing/subscription questions
- 40% annual agent turnover from burnout
How We Solved It
Ticket Analysis
Audited 500K+ support tickets, classified into 127 intent categories. Identified that 73% were repetitive and automatable with high confidence.
Training Data Curation
180K real conversation examples curated and cleaned. Built evaluation datasets for each of 127 categories with edge case coverage.
Multi-Agent Architecture
Billing Agent, Technical Agent, Account Agent, and Escalation Agent — each specialized with domain-specific tools and knowledge bases.
Integration Pipeline
Connected to Zendesk for ticket management, Stripe for payment actions, internal APIs for account operations. Real-time context enrichment.
Quality & Safety
Human-in-the-loop for low confidence (<85%), mandatory escalation for harassment/legal. A/B tested AI vs human: AI scored higher on CSAT.
Our Implementation
orchestrator.py
# WebCoreLab — Multi-Agent Support Orchestrator
import anthropic
from enum import Enum
class TicketCategory(Enum):
BILLING = "billi
g"
TECHNICAL = "tech
ical"
ACCOUNT = "accou
t"
HARASSMENT = "harassme
t" # Always huma
class TicketRouter:
asy
c def route(self, ticket: dict) -> dict:
classification = await self.classify(ticket['message'])
# Ma
datory huma
escalatio
if classification['category'] i
['harassme
t', 'legal']:
return await self.escalate_human(ticket)
if classification['co
fide
ce'] < 0.85:
return await self.escalate_human(ticket)
age
t = self.get_age
t(classification['category'])
response = await age
t.ha
dle(ticket)
await self.log_i
teraction(ticket, response)
return response
# 847,293 tickets processed i
first 90 days
# Auto
omous resolution: 96.8%
# Huma
escalatio
s: 3.2%
# Average response: 2.7 seco
ds
Measurable Impact
Measured 90 days after full deployment
“We were skeptical about replacing 2,000 agents with AI. WebCoreLab proved us wrong — not only did response times drop from hours to seconds, but customer satisfaction actually improved. The system handles billing disputes, technical issues, and account questions with a level of consistency no human team could match.”
— A.R., VP Customer Experience, Dating Platform (NDA)