ConvoLens

Transforms SRT transcripts into insights with sentiment + behavior analytics (latency, empathy, etc.). Includes dashboard & API, integrates easily, and extends to Voice AI for real-time monitoring.

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🚀 ConvoLens: Conversation Intelligence Engine

💡 Inspiration

As customer interactions increasingly shift toward AI-driven and automated systems, monitoring and improving conversation quality has become a major challenge.

Most teams still rely on manual reviews or basic metrics, which fail to capture how conversations unfold—especially tone, timing, and behavioral patterns.

ConvoLens was inspired by the idea of turning raw conversations into measurable intelligence, enabling teams to proactively improve both human and AI-driven interactions.

🔍 What it does

ConvoLens transforms SRT conversation transcripts into actionable insights using AI.

It:

  • Tracks sentiment across the conversation timeline (emotional arc)
  • Detects response latency and silence spikes
  • Measures empathy and ownership language
  • Analyzes agent behavior patterns like talk ratio, monologues, and filler usage

The system outputs:

  • 📊 An interactive dashboard
  • 🔌 A REST API for real-time scoring
  • 📄 An executive report for decision-making

⚙️ How we built it

We built ConvoLens entirely using Zerve with a structured, prompt-driven workflow:

1. Data Parsing

SRT files are converted into structured conversation turns with:

  • speaker
  • timestamps
  • text

2. Sentiment Analysis

Each conversation turn is classified as positive, negative, or neutral to reconstruct the emotional progression.

3. Behavioral Conversation Metrics

We engineered features to quantify how conversations are handled, including:

  • Response latency
  • Silence spikes
  • Agent talk ratio
  • Filler word ratio
  • Empathy phrase rate
  • Ownership language score

4. Deployment

  • Built a Streamlit dashboard for exploration
  • Exposed a REST API for integration
  • Generated a self-contained HTML executive report

⚠️ Challenges we ran into

  • Unstructured Data Handling SRT files vary in format, requiring robust parsing and cleaning

  • Context Preservation Maintaining conversation flow across turns for accurate analysis

  • Feature Engineering Designing meaningful behavioral metrics like empathy and ownership scoring

  • Scalability Running multiple analyses efficiently across conversations

🏆 Accomplishments that we're proud of

  • Built an end-to-end pipeline from raw transcripts to insights
  • Combined sentiment and behavioral analytics for deeper understanding
  • Delivered a production-ready system (dashboard + API + report)
  • Designed a system extensible to Voice AI applications

🧠 What we learned

  • Sentiment alone is not enough—behavioral signals add critical context
  • Response timing has a strong impact on conversation outcomes
  • Escalations often follow predictable patterns
  • Structuring unstructured data unlocks meaningful insights

🔮 What's next for ConvoLens

  • Integration with Voice AI systems via speech-to-text
  • Real-time conversation monitoring and alerting
  • Automated coaching recommendations for agents
  • Feedback loops for improving AI-driven conversations
  • Scaling the system for enterprise use

Built With

  • zerve
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