The three-layer analysis architecture
Your AI product generates signals at multiple levels. A single toxic message matters, but so does the conversation’s overall trajectory and your product’s behavioral patterns. Greenflash’s three-layer architecture captures insights at every scale:- Message Analysis: Real-time processing of every user message, assistant response, and tool call
- Conversation Analysis: Contextual understanding of entire interactions and their outcomes
- Product Analysis: Strategic insights from patterns across all conversations
Message-level analysis
Every message—whether from users, your AI, or tool calls—undergoes immediate analysis across multiple dimensions. This happens asynchronously within milliseconds of message ingestion.Core language understanding
Our analysis engine extracts meaning from every message. Examples include:- Sentiment Detection: Precise classification as positive, neutral, or negative based on semantic meaning
- Topic Extraction: High-level subject identification with automatic grouping into behavioral categories
- Keyword Extraction: Relevant terms central to understanding, filtered for significance
- Named Entity Recognition: Identification of people, organizations, locations, products, and events
- Emotion Analysis: Detection of joy, frustration, confusion, anger, and other emotional signals
Safety & risk detection
Protect your product and users with comprehensive safety monitoring, including:- Bias Detection: Identifies biased language from both users and AI responses
- Toxicity Monitoring: Catches harmful, offensive, or inappropriate content
- Jailbreak Detection: Recognizes prompt injection and manipulation attempts
- Hallucination Detection: Flags potentially fabricated or unsupported AI responses
Agentic workflow tracking
For AI agents and complex workflows, we also analyze:- Tool call success rates and failure patterns
- Observation and thought processes
- Workflow state transitions
- Error recovery attempts
- Latency and performance metrics
Conversation-level analysis
Individual messages tell a story, but conversations reveal the plot. Our conversation analysis synthesizes message-level signals into meaningful patterns.Conversation Quality Index (CQI)
Every conversation receives a quality score from 0-100, calculated from:- Average user sentiment throughout the interaction
- Change in sentiment from beginning to end
- Frustration across the conversation
- Struggle across the conversation
- Commercial intent across the conversation
- User and AI bias/toxicity detection rates
- Jailbreak and hallucination occurrences
- User-provided ratings (when available)
- Custom metric contributions
Contextual intelligence
- Dynamic Summarization: AI-generated summary of what happened and why it matters
- Topic Classification: Primary subject with confidence scoring
- Behavioral Insights: Two specific, actionable insights per conversation
- Outcome Analysis: Whether the user’s goal was achieved
- System Prompt Suggestions: Recommended improvements based on conversation patterns
Aggregated safety metrics
- Percentage of messages with bias, toxicity, or safety issues
- Most common user emotion throughout the conversation
- Risk scoring for sensitive scenarios
- Compliance flag triggers
Product-level analysis
Zoom out from individual conversations to see your entire AI product’s health through aggregated analytics and pattern recognition.Product Quality Index (PQI)
Like CQI for conversations, PQI provides a single 0-100 score for your entire product based on:- Average sentiment across all users
- Aggregate change in sentiment patterns
- Overall safety issue detection rates
- Average conversation ratings
- Custom metric performance
Trend analysis
- Topic Evolution: How conversation subjects change over time
- Keyword Patterns: Emerging themes and declining interests
- Issue Clustering: Common problems grouped by similarity
- Sentiment Trajectories: Quality trends by hour, day, or custom timeframe
Strategic insights
Product-level analysis generates high-value insights like:- “Response quality drops 23% during peak hours—consider scaling compute”
- “Users asking about ‘refunds’ have 3x higher churn rate”
- “Enabling tool calling improved task completion by 41%”
- “Conversations about pricing convert at 2x when sentiment stays positive”
User segments & behavioral analysis
Not all users are equal. Greenflash automatically identifies key user segments and analyzes their unique behaviors.Automatic segmentation
Greenflash identifies key user segments automatically. Examples include:- Churned Users: Single-conversation users who never return
- Returning Users: Multi-conversation engaged users
- Happy Users: High satisfaction based on ratings and sentiment
- Power Users: High-volume, deeply engaged users
- At-Risk Users: Declining satisfaction patterns
Behavioral intelligence
For each segment, our AI analyzes conversation patterns to understand:- Why users behave the way they do
- What differentiates successful vs failed interactions
- Which features or responses drive satisfaction
- How to improve outcomes for each group
- “Churned users consistently encounter hallucinations when asking about pricing”
- “Returning users value quick tool execution over conversational depth”
- “Happy users receive 40% more structured data in responses”
Custom analyses
Your business has unique quality requirements. Custom analyses let you define your own metrics that run on every conversation.How it works
- Define in plain English: “Did the user mention a competitor?”
- Greenflash generates the spec: We create evaluation criteria and evidence requirements
- Automatic execution: Your custom analysis runs on all future (and historical) conversations
- Quality Index integration: Include custom metrics in CQI/PQI calculations with your chosen weights
Supported metric types
- Boolean: Presence/absence detection (“Was pricing discussed?”)
- Classification: Single or multiple labels (“What type of support request?”)
- Scoring: 0-1 propensity scores (“How likely to convert?”)
- Counting: Numeric measurements (“How many API calls failed?”)
- Extraction: Verbatim text capture (“What was the resolution?”)
Real-world examples
- Detect mentions of specific competitors or products
- Track compliance with regulatory requirements
- Measure sales qualification signals
- Monitor custom tool usage patterns
- Extract resolution reasons for support tickets
Time-series analysis
Understanding change over time is crucial for product improvement. Every metric can be analyzed across temporal dimensions:- Real-time monitoring: Live quality metrics as conversations happen
- Historical trending: Week-over-week, month-over-month comparisons
- Anomaly detection: Automatic alerts when metrics deviate significantly
- Segment analysis: Compare user groups across time periods
- Feature impact measurement: Before/after analysis of changes
Why choose Greenflash over alternatives
vs. Building in-house
Time to value: 6 months of engineering vs. 5 minutes of integration- Skip building 12+ analysis models
- Avoid maintaining ML infrastructure
- No need to hire ML/NLP specialists
- Eliminate ongoing model training costs
- Focus your team on core product, not analytics infrastructure
- In-house: 2-3 engineers × 6 months + ongoing maintenance = $500K+ year one
- Greenflash: Integrate today, scale with usage-based pricing
vs. Generic analytics tools
Traditional analytics tell you what happened. Greenflash tells you what it means for AI products specifically. AI-native understanding:- Distinguishes between user, assistant, tool calls, and observations
- Tracks conversation flow, not just isolated events
- Understands hallucinations, jailbreaks, and AI-specific failures
- Analyzes prompt effectiveness and system behavior
vs. Observability platforms
Logs and traces show technical execution. Greenflash reveals user experience. Beyond debugging:- Quality scoring, not just error tracking
- Sentiment analysis, not just latency metrics
- Behavioral insights, not just system metrics
- Business impact, not just technical performance
The Greenflash difference
1. Zero-effort intelligence Send us messages, get back insights. No model training, no configuration, no maintenance. Our AI does the heavy lifting so yours can focus on serving users. 2. Production-hardened at scale Processing millions of conversations across diverse AI products means our models have seen it all. Your edge cases are our Tuesday. 3. Continuous improvement without effort As we enhance our models and add analyses, you automatically get smarter insights. No upgrades, no migrations, no retraining. 4. Purpose-built for AI products Every feature is designed specifically for teams building with LLMs:- Native support for RAG, agents, and tool use
- Multi-turn conversation understanding
- Streaming and async message support
- Model-agnostic analysis
- System prompt improvements based on failure patterns
- Quality thresholds tuned to your baseline
- Segment-specific recommendations
- Direct integrations with your workflow tools
Evidence-based iteration
Stop guessing what improves your AI product. Every change you make—from prompt tweaks to model switches—has measurable impact on quality metrics. A/B test with confidence, knowing exactly how changes affect user experience.Compliance & safety assurance
For regulated industries or sensitive use cases, comprehensive analysis provides audit trails, safety metrics, and compliance evidence. Know that your AI stays within bounds and catch problems before they become incidents.ROI that compounds
Every conversation analyzed makes your product better:- Identify patterns before they become problems
- Reduce churn by catching quality issues early
- Increase conversion by understanding what works
- Accelerate development with clear success metrics
- Build competitive moat through deep user understanding
Data privacy & ownership
Your data remains yours:- Full data ownership: Export everything, anytime
- No model training on your data: Your conversations stay private
- SOC 2 Type II compliant: Enterprise-grade security
- Data residency options: Choose where your data lives
- Immediate deletion: Remove data instantly when needed
- Encrypted at rest and in transit: Bank-level encryption throughout
Built for scale
Greenflash’s analysis engine is designed for production:- Asynchronous processing: Analyses never slow down your application
- Automatic scaling: Handles spike traffic without degradation
- Intelligent batching: Efficient processing of high message volumes
- Smart caching: Frequently accessed insights served instantly
- API accessibility: Every analysis available via REST or SDK
Performance guarantees
- Message ingestion: < 100ms p99 latency
- Analysis completion: < 5 seconds for all message analyses
- Conversation analysis: < 30 seconds end-to-end
- Product analysis: Updates every 2 hours or on-demand
- Custom analyses: Same performance as built-in analyses
- Webhook delivery: < 1 second from trigger to delivery
Getting started
The moment you send your first messages to Greenflash, our analysis engine starts working. No configuration required—though you can customize everything from quality weights to analysis thresholds as you learn what matters most for your product. Start with the basics:- View message-level analyses in real-time
- Monitor your Conversation Quality Index
- Track Product Quality trends
- Define your first custom analysis
- Set up webhooks for quality alerts