Proving AI Agents Can Handle Real Customers
A Tier 1 bank needed to validate AI agents securely before deploying them in live customer support.
Outcomes
1000+
Concurrent Call Capacity
30% - 40%
Calls Fully Handled by AI
90%
Escalation Accuracy
85%
Language & Accent Recognition Accuracy
Technology Vendors Suited to Evaluation
Business Problem
The bank was under pressure to modernise its contact centre. Call volumes were rising, costs were increasing, and customers expected faster, more personalised service. Leadership saw AI agents as a way to improve responsiveness and efficiency, but the risks were high. Legacy systems made integration and testing difficult, and strict data governance rules meant real customer information couldn’t be used.
There was also uncertainty around how AI agents would perform with real-world accents, languages, and call volumes. Without solid evidence, the cost of failure in live service was too high to justify the rollout.
Challenges
- Legacy Integration Complexity: Difficult to validate AI agents against existing tech stack disrupting live operations.
- Data Privacy & Compliance: Real customer conversations couldn’t be used, limiting safe training and evaluation.
- Language & Accent Variability: Uncertainty whether AI agents could reliably understand diverse voices and multilingual queries.
- User Experience & Escalation: No way to evidence how agents would handle mis-detections, handovers, and compliance-critical flows.
- Scale & Performance Risk: Lack of evidence on whether AI could manage thousands of concurrent calls without service degradation.
From Idea to Evidence with NayaOne
NayaOne enabled the bank to move from concept to validated evidence by running Contact Centre AI Agents in a secure, production-like sandbox that mirrored legacy integrations.
- Enterprise Gateway + Workspaces: Vendors were hosted in Azure-based workspaces with a mock Microsoft Dynamics CRM environment, allowing safe integration without touching live systems.
- Synthetic Data Generation: Realistic customer and product datasets were created to simulate end-to-end multi-channel journeys without exposing sensitive data. This gave Product, Ops, and Legal teams visibility into UX flows, intent detection, escalation handling, and compliance pathways.
- Realistic Call Simulation: AI agents were tested against inbound and outbound call scenarios, including different languages and diverse accents, ensuring performance across varied customer contexts.
- Performance & Stress Testing: The sandbox supported high-volume simulations – up to 1,000 concurrent calls – to benchmark system resilience, latency, and accuracy at scale.
By combining controlled integrations, synthetic journeys, and high-fidelity call simulations, the bank gained hard evidence of how AI agents would perform in production, reducing risk and accelerating executive confidence in adoption.
Impact Metrics
PoC Timeline Reduction
8 weeks with NayaOne vs 9 – 12 months traditionally
Time Saved in Vendor Evaluation
6 - 7 months
Decision Quality
Improved with full-journey visibility, ensuring buy-in from Product, Ops, and Legal.
KPIs
- Intent Detection Accuracy (%) – how often the AI correctly understands the customer’s need.
- Resolution Rate (%) – % of customer queries resolved without escalation to a human.
- Language & Accent Recognition Accuracy (%) – ability to handle diverse voices and multilingual queries.
- Call Containment Rate (%) – proportion of calls fully managed by AI without human intervention.
- Concurrent Call Capacity – maximum number of calls handled simultaneously without degradation.
- Escalation Accuracy (%) – correct handover to human agents when required, avoiding customer frustration.
Validate Contact Centre AI Agents Before Enterprise Rollout
Test AI agents to cut handling time, improve resolution rates, and support human agents under governed conditions.
