Project Overview
One of the world’s leading customer experience platforms partnered with Cubet to develop an intelligent system that could go beyond understanding customer queries. They wanted a solution that could interpret how something was said, not just what was said, detecting emotional cues like frustration, sarcasm, confusion, or stress in real time. The solution needed to rank each interaction by severity, learn continuously from evolving behaviour, and trigger the right responses without human bottlenecks. This was not just about customer service—it was about protecting brand experience at scale, while delivering emotionally aware support across voice and chat channels.
Industry
Customer Experience Technology / Enterprise SaaS
The Client
A global enterprise operating a high-volume customer engagement platform. Their tools power customer support operations for businesses across industries, handling thousands of conversations every day through chat and voice channels.
Challenges Addressed
Traditional support systems were primarily designed to capture keywords and match patterns. This meant they often fell short of recognising customer frustration until it escalated into formal complaints or churn. Subtle tonal shifts, such as sarcasm masked in polite language or a sudden switch from calm to curt, went completely unnoticed. The client needed more than sentiment analysis—they needed real-time emotional intelligence that could surface red flags, prioritise issues before they became problems, and give their teams the ability to intervene faster and smarter.
The key requirements included:
- Real-time tone detection across chat and voice conversations
- Identification of behavioural anomalies based on customer history
- Severity scoring of interactions for dynamic response handling
- Automated triggers to alert teams, escalate issues, and initiate follow-ups
- A feedback loop allowing human supervisors to refine and improve model accuracy over time
Collaboration in Action
Cubet delivered a production-grade, AI-powered platform that analyses customer conversations across multiple channels. It processes both text and transcribed voice inputs to detect emotional tone, flag deviations from normal behaviour, and take automated actions in response to severity scoring. At the heart of the solution is a continuously learning engine that evolves with the customer base, adapting to shifts in tone and behavioural patterns over time. This allows businesses to act on potential issues within seconds—not hours or days.
The entire system integrates tightly with existing CRM and support tools, ensuring no disruption to workflows while vastly improving responsiveness. Human-in-the-loop supervision adds a safety net, with manual overrides feeding back into the model to enhance its accuracy and reduce false positives.
Technologies Deployed
- Frontend: A React-based dashboard giving support managers full visibility into flagged conversations, live alerts, and severity breakdowns. Filters allow teams to drill down by tone, customer segment, or trigger type.
- Backend: Python with FastAPI orchestrates services, while Celery and Redis manage asynchronous task queues.
- Data Storage: PostgreSQL for structured data, MongoDB for unstructured transcripts and behavioural logs.
- AI/ML Stack:
- Transformer models (BERT, RoBERTa) fine-tuned on emotional tone datasets for text classification
- Whisper for real-time voice-to-text transcription
- Isolation Forests and LSTM Autoencoders to detect time-series anomalies in behaviour and tone
- PyTorch and scikit-learn used for model training, tuning, and deployment
- Automation: Webhook-triggered workflows integrated with Slack, Microsoft Teams, Zendesk, Salesforce, and other custom CRMs. A rule engine dynamically adjusts responses based on severity thresholds.
- Infrastructure: Deployed on AWS with Kubernetes-based autoscaling for inference services. Full end-to-end encryption, role-based access controls, and audit logging ensure enterprise-grade security and compliance.
Innovative Feature
What sets this solution apart is the layered intelligence approach. A hybrid system blends deep learning for tone detection with statistical anomaly detection for behavioural shifts. It doesn’t rely on keywords—it reads between the lines. The system evaluates tone in context, understands frequency and escalation patterns, and calculates severity based on a mix of historical data and current interaction context.
Flagged conversations are handled based on a weighted scoring logic that includes tone deviation, message length, user behaviour history, and issue recurrence. Automated workflows can escalate to senior agents, send alerts to relevant teams, or trigger direct follow-ups via email or chatbots.
To ensure accuracy and avoid overreaction, the system:
- Ignores low-confidence predictions while still flagging them for review
- Applies sarcasm and humour detection models to reduce false alerts
- Falls back on anomaly detection if tone classification confidence is low
- Logs all manual overrides and incorporates them into bi-weekly retraining sessions
Value Delivered
- 60% of high-severity follow-ups are now triggered automatically within 30 seconds of tone detection
- 25% reduction in escalated complaints due to early intervention
- QA teams report 40% faster triage of flagged issues
- Net Promoter Score (NPS) improvement was most visible among customer groups prone to emotional volatility, segments that previously went unmonitored
User Feedback
Supervisors praised the system for surfacing hidden problems that would otherwise slip through. The human-in-the-loop feature was particularly appreciated—it allows teams to remain in control, while still benefiting from the speed and scalability of automation. Feedback also highlighted how much more proactive the support team had become, with less time spent reacting to complaints and more time preventing them.
Conclusion
By merging tone detection with behavioural analytics and embedding it deeply into customer workflows, Cubet delivered more than just an AI system—it built a new layer of emotional intelligence into the support process. The result is a smarter, faster, and more empathetic customer experience that doesn’t rely solely on human effort.
Impact Made
This solution marked a significant leap forward in how emotional context is understood in customer communication. It gave the client an edge in both operational efficiency and customer satisfaction, proving that when technology listens for how things are said, it hears what really matters.