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Examples & Queries

The Examples & Queries section provides practical, query-driven illustrations of how TrueAI entities can be used to understand data quality, revenue outcomes, value distribution, and rep behavior.

Each category below contains multiple examples, each following a consistent structure:

  • Description
  • Query
  • Sample Output

These examples are intended to make platform behavior transparent and reproducible.

Automatic Data Enhancement (ADE)​

Transparency on the Data Cleanup Process

Before using derived entities, it is important to understand the quality of the underlying source data and how it has been enhanced by the TrueAI platform.

The examples in this section illustrate how raw records are deduplicated, consolidated, and normalized, and how these transformations impact downstream analysis.

Examples in this category focus on:

  • Lead and company deduplication
  • Master record identification
  • Consolidation logic behind derived entities such as hat_leads

See also:

Deal Intelligence (DI)​

Revenue & Buyer Journey Insights

Understanding deal outcomes requires the ability to isolate specific steps in the sales process and analyze the customer and deal attributes that influence success.

This category demonstrates how the TrueAI platform enables this analysis using the ssr and ssr_history entities.

Examples in this category focus on:

  • Buyer journey progression
  • Deal stage transitions
  • Revenue outcome drivers

See also:

Value Intelligence (VI)​

Fairness & Value Creation in the GTM Organization

Rep performance cannot be evaluated in isolation without understanding how value is distributed across the organization.

This category explores how leads, accounts, and opportunities are allocated, and how access to high-potential pipeline often correlates with top performance.

Examples in this category focus on:

  • Lead and account value distribution
  • Pipeline creation sources
  • Structural advantages and fairness considerations

See also:

Coaching Intelligence (CI)​

Rep Skills & Behavior Analysis

Effective coaching requires a clear understanding of current rep behavior and skill development.

This category highlights how previously unavailable behavioral data points can be used to support individualized coaching and evaluate the impact of training programs and tools.

Examples in this category focus on:

  • Rep activity patterns
  • Skill progression signals
  • Coaching and enablement effectiveness

See also: