European financial services provider cuts customer support OpEx by 85%

Scaling Multilingual Support

With over 250,000 inbound tickets per year arriving through email and chat across 10+ languages, the support operation faced a structural scaling problem:

• Every new ticket required a human to translate the language, identify the issue type, and assign it to the right team.
• Each inquiry spanned a wide operational surface: account verifications, payment disputes, card blocks, transaction inquiries, regulatory compliance requests. Classification covered distinct categories, while a misrouted ticket meant a second touch, a longer queue, and a missed expectation. 
• At 32-hour average resolution times, the operation was absorbing cost at a rate that could not sustain the company’s growth trajectory.

The trajectory was the real threat. Ticket volumes were growing year over year and every miss carried possible churn risk and penalties. And the same scaling dynamics applied across other operational functions across the enterprise. 

The company had attempted to address this before, but previous approaches couldn’t maintain accuracy across languages, adapt to new ticket types without manual reconfiguration, or scale without proportional engineering effort.

This was more than an optimization problem. The manual model couldn’t sustain the business at its current growth rate, and conventional automation had already failed to close the gap.

What was required was a fundamentally different operating model: one where autonomous agents handled classification and routing without human intervention, and human specialists focused exclusively on the exceptions that required judgment. Multilingual support was the proving ground, and the ambition was enterprise-wide.

Autonomous AI Agents for Support Operations

The company aligned on a single objective of achieving autonomous classification, routing across all inbound multilingual support tickets at accuracy rates exceeding human performance while maintaining full auditability for regulatory compliance. 

This initial deployment would establish the operational blueprint for extending autonomous execution across the broader enterprise.

Otera deployed coordinated autonomous agents across the support workflow:

• Language Detection & Translation Agent identifies the inbound language and normalizes the ticket content for downstream processing, covering languages without requiring separate rulesets per language 
• Classification Agent maps each inquiry to one of dozens of categories, interpreting unstructured text across languages to determine issue type, urgency, and required expertise 
• Routing Agent assigns the classified ticket to the appropriate specialist queue based on issue type, language, agent availability, and priority 
• Escalation Agent identifies edge cases that fall outside confident classification thresholds and routes them to human reviewers with full context: the agent’s reasoning and a recommended classification 

These agents operate as a coordinated pipeline: a ticket arriving in any supported language moves from detection through classification, routing, and, if needed, escalation without human intervention. 

Tickets complete this pipeline autonomously, and the remaining cases where confidence thresholds are not met or where the inquiry falls outside established categories, route to human specialists with full decision context attached.

Every classification decision is logged with the agent’s reasoning, confidence score, and routing rationale. This gives compliance teams a complete, searchable audit trail for any ticket in any language, meeting the regulatory transparency requirements of the financial services environment.

After deployment, the system transferred operational control to the support team and supervisors adjust classification rules, refine routing logic, and review exception patterns directly, without requiring engineering support or vendor intervention.

Annual Support Costs Dropped 85%

Within the initial deployment scope, the shift to autonomous execution delivered measurable impact across cost, speed, and operational capacity within six weeks of deployment:

• Multilingual tickets now process autonomously from intake through classification and routing, improving CSAT +20%.
• Annual support costs across this function dropped 85%, driven by autonomous execution replacing manual classification headcount. 
• Resolution time dropped from 32 hours to 5 hours, a 6x improvement. Classification and routing, previously the bottleneck, now complete in under 90 seconds.  The remaining resolution time reflects specialist handling of exception cases and downstream response 
• This initial deployment established the operational blueprint for enterprise-wide expansion.