AI Quality Assurance

Monitor and Improve Your AI Data Quality

Continuously improve your AI's performance with real-time relevancy scoring and blind spot detection. Track retrieval quality, optimize responses, and identify improvement areas.

Retrieval Performance Metrics

Relevancy Score94.2%
Query Rewriting Effectiveness87.5%
Coverage (No Blind Spots)91.8%

Real-time Insights

Know exactly how your AI data performs

Our performance monitoring dashboard gives you actionable insights into your retrieval quality, helping you maintain high-quality AI responses.

Relevancy Scoring

Monitor the relevance of retrieved chunks in real-time with scores from 0-1, tracking how well your data matches user queries.

Query Rewriting Analysis

Compare performance between original and rewritten queries to optimize your AI's understanding of user intent.

Overall Health Metrics

Get a unified view of your retrieval system's performance with color-coded health indicators and trend analysis.

Data Blind Spot Detection

Identify gaps in your knowledge base by analyzing low-relevance queries and missing content patterns.

Time Series Analytics

Track performance changes over time with interactive visualizations to spot trends and anomalies.

Top 2 Chunk Focus

Measure what matters most - the relevance of your top-ranked results that directly impact AI response quality.

Interactive Dashboard

Monitor performance at a glance

Our color-coded dashboard makes it easy to spot issues and track improvements over time.

Non-Rewritten Relevance

0.78

High relevance

Rewritten Relevance

0.85

+9% improvement

Overall Health

0.82

Excellent

Performance Trend

Interactive time series visualization

Comprehensive metrics for data quality

Track key performance indicators that directly impact your AI\'s response quality.

Non-rewritten question relevance scores
Rewritten question performance improvements
Overall retrieval health indicators
Usage tracking for retrievals and advanced operations
NDCG (Normalized Discounted Cumulative Gain) metrics
Average relevancy across all queries

Why focus on top 2 chunks?

Our monitoring focuses on the top 2 retrieved chunks because that\'s what matters most for AI accuracy:

  • RAG systems primarily use the most relevant chunks to generate responses
  • Measuring too many chunks dilutes metrics and masks real performance issues
  • High relevance in top results directly correlates with accurate AI responses
  • Provides clean, actionable signals for system health

Start monitoring your AI data quality

Get real-time insights into your retrieval performance and continuously improve your AI's accuracy.