Have you ever wished you could read your customers’ minds? While we haven’t quite mastered telepathy, our recent hackathon project might be the closest thing to it. In a few intense days of coding and caffeinating, our team built Vibecheck—a sentiment analysis tool that's changing how we understand customer interactions, allowing us to add actionable insights and trigger events.

The Challenge

Understanding customer sentiment is inherently complex. Whether through support tickets, chat conversations, phone calls, or video meetings, customers constantly provide feedback about their experience—both explicitly and implicitly—across multiple platforms. Traditional methods of tracking customer satisfaction often miss subtle cues and struggle to provide a comprehensive view across different communication channels. This fragmentation makes it challenging to identify patterns and take proactive action.

What is Vibecheck?

Vibecheck emerged as our answer to this challenge. Think of it as an emotionally intelligent AI assistant that never sleeps, never gets tired, and never misses a subtle cue in customer communications. It’s like having a super-powered empathy engine working tirelessly behind the scenes, processing and analyzing every customer interaction while safeguarding privacy.

The tool integrates with multiple communication channels:

💬 Real-time chat analysis

📧 Email sentiment tracking

📞 Voice interaction analysis

🤝 Video conference insights

But Vibecheck doesn’t just track whether a customer is happy or unhappy; it delves much deeper. The system understands ten distinct emotional states, ranging from satisfaction and trust to frustration and skepticism. This nuanced approach helps teams understand not just what customers are saying but how they truly feel about their experiences.

Under the Hood: The Architecture of Empathy

We take data privacy and security seriously every step of the way. Customer data is anonymized and encrypted during processing to meet global standards. We know our customers trust us, and keeping their information safe is at the heart of what we do.

The magic of Vibecheck happens in several carefully orchestrated stages. Let's walk through how a customer interaction becomes actionable intelligence:

Stage 1: Data Collection and Normalization

When an interaction ends—whether it's a closed support ticket, completed chat, or finished call—Vibecheck springs into action. First, it normalizes the data into a consistent format, regardless of the source.

Each platform is handled with specific considerations:

1. Chat: Captures completed chat interactions, processes message threads and response times, and tracks conversation closure and resolution states.
2. Email Processing: Analyzes complete email threads and monitors resolution pathways and outcomes.
3. Voice Interactions: Processes call transcripts, analyzes call duration and response times, and currently requires manual transcript feeding (automation planned for the future).
4. Video Meetings: Handles meeting transcripts from provided PDF documents and captures participant interactions.

Stage 2: The Analysis Process

Our analysis process, starting simply in version 1, primarily uses an LLM model to process normalized data. Looking ahead, we plan to blend NLP (traditional language processing) tools and LLM AI models like Claude or OpenAI to get the best of both worlds. The system breaks down conversations into manageable components, ensuring we capture all critical details along the way.

Each step remains separate and focused, allowing our AI to deliver reliable answers by working with concise instructions.

Emotional Context Detection

Our system performs multi-layered analysis to understand the emotional context.

1. Primary Emotion Classification:

• Satisfaction: Expresses contentment and confidence with the service or product.
• Frustration: Results from unmet expectations, delays, or issues that block progress.
• Confusion: Occurs when there’s uncertainty about processes, features, or outcomes, requiring clarification.
• Trust: Reflects confidence in the company’s ability to deliver and maintain value.
• Concern: Arises when there’s worry about potential issues, future services, or product quality.
• Urgency: Demands immediate attention due to pressing needs or problems.
• Disappointment: Felt when expectations aren’t met or the service/product fails to deliver.
• Excitement: Shows eagerness and enthusiasm, often about new features, upgrades, or achieved successes.
• Skepticism: Reflects doubt or caution about promises or new developments.‍
• Gratitude: Demonstrates appreciation for the service, help, or results provided.

2. Contextual Analysis:

Each interaction is further categorized through:

• Primary Category Mapping: High-level classification (e.g., "Delivery," "Product," "Support").
• Sub-category Classification: Specific issue areas (e.g., "Order Fulfillment," "Feature Request").
• Severity Assessment: Scale of 1–5, with higher ratings indicating conversations requiring immediate resolution.

Sentiment Progression Analysis

Vibecheck tracks sentiment evolution throughout interactions using temporal sentiment mapping:

This progression tracking helps identify:

• Initial sentiment baselines.
• Impact of responses on sentiment.
• Effectiveness of resolutions.
• Overall success of interactions.

Resolution and Efficiency Analysis

The final stage evaluates the effectiveness of interaction handling:

1. Resolution Metrics:

• Outcome documentation
• Resolution type classification
• Duration tracking
• Monitoring of first response time

2. Efficiency Indicators:

• Categorization of response times
• Measurement of customer satisfaction
• Process efficiency scoring

3. Action Item Generation:

• Automated suggestions based on patterns
• Process improvement recommendations
• Tracking of follow-up actions

Output and Automated Actions

Let’s look at how Vibecheck structures its analysis and enables automated responses. Here’s an example of a complete sentiment analysis output:

Automated Triggers and Notifications

This structured output enables powerful automation through configurable triggers, offering endless possibilities to enhance the customer experience and uncover patterns in our feedback.

For example:

1. Severity-Based Alerts:

1. SMS notifications to supervisors for high-severity cases (via Twilio)
2. Slack alerts to team leads for urgent follow-ups
3. Email digests for trend analysis

2. Sentiment-Based Actions:

• Positive sentiment triggers automatic kudos in team Slack channels
• Declining sentiment progression initiates a supervisor review
• Sustained negative sentiment escalates the issue to account management

3. Pattern Recognition:

• Automated reporting on recurring issues by category
• Early warning system for potential systemic problems
• Notifications to the customer success team for accounts showing concerning patterns

4. Response Time Triggers:

• Escalation alerts for delayed first responses
• Notifications for extended resolution times
• Recognition for exemplary response times

This combination of detailed analysis and automated responses ensures that no critical interaction falls through the cracks and that both problems and successes receive the appropriate attention.

Conclusion

Vibecheck represents a significant step forward in understanding customer sentiment. By combining advanced sentiment analysis with practical action items, we're not just collecting data; we're building stronger, more meaningful relationships with our customers.

The success of this hackathon project demonstrates the power of focused innovation and the impact that thoughtful technology can have on customer experience. As we continue to develop and refine Vibecheck, we look forward to seeing how it will further transform our understanding of customer interactions.