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Greenflash helps you build training datasets from real production conversations, filtered and organized by comprehensive quality analysis.

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Intelligent data selection

Greenflash’s analysis engine identifies which conversations make good training examples and which demonstrate patterns to avoid.

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Quality-based curation

Export conversations filtered by:
  • Quality scores (high for positive training, low for negative examples)
  • User ratings and satisfaction metrics
  • Edge cases and unusual patterns
  • Specific topics or use cases
Every export includes:
  • Full conversation transcript
  • Quality metrics and analysis scores
  • User ratings and feedback
  • Topic classification and keywords
  • Sentiment progression
  • Safety issue flags

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Custom filtering

Build datasets that match your exact training needs:
  • Filter by sentiment trajectory (improving vs. declining)
  • Select specific error types (hallucinations, refusals, confusion)
  • Choose conversation lengths and complexity levels
  • Include only rated conversations with explicit feedback
  • Combine multiple criteria for precise curation

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Training data that matters

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Positive examples

Greenflash identifies what makes conversations successful:
  • High user satisfaction scores
  • Positive sentiment throughout
  • Successful task completion
  • Efficient problem resolution
  • No safety or quality issues

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Negative examples

Learn from what goes wrong:
  • Hallucination occurrences with context
  • Sentiment drops with triggering messages
  • Failed task attempts with breakdown points
  • Safety violations with specific patterns
  • User frustration signals

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Balanced datasets

Greenflash helps create representative training sets:
  • Topic coverage across categories
  • Varied conversation complexity
  • Balance of positive and negative examples
  • Representative user interaction patterns

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Export capabilities

Each exported conversation includes:
  • Complete message transcripts with metadata
  • Quality scores and analysis results
  • Sentiment, topic, and safety annotations
  • Tool calls and system prompts
  • User ratings and feedback
Available formats:
  • CSV for analysis and filtering
  • JSON for model training pipelines
  • Custom schemas for your requirements

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Workflow

  1. Deploy your AI with Greenflash monitoring
  2. Accumulate and analyze real conversations
  3. Export curated datasets based on quality metrics
  4. Fine-tune models with production data
  5. Measure improvement and iterate

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Privacy and compliance

  • Data ownership: Your conversations remain yours
  • Filtering controls: Exclude sensitive information
  • Anonymization: Remove PII before export
  • Audit trails: Track what data was exported when
  • Access controls: Limit who can export data

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Common use cases

  • Fine-tuning: Select high-quality conversations for model training
  • Evaluation: Build test sets from real edge cases
  • Error analysis: Export and study failure patterns
  • RLHF: Curate examples with clear quality signals