CASE STUDIES

AI-native applications

Price the model before it prices you.

Companies selling AI applications grow fast into an unsolved question: what a seat, a credit, or an outcome should cost when every answer carries an inference bill. The engine reads billing, usage, and model spend together, so pricing and packaging moves are scored on margin per account rather than on adoption alone.

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READS FROM

  • STRIPE
  • SNOWFLAKE
  • GA4
  • HUBSPOT
  • GOOGLE ADS
  • LINKEDIN ADS

DECIDES ON

  • Margin per account
  • Net revenue retention
  • Pricing model fit
  • Activation to production
  • Renewal risk

ANALYSES

  • Pricing power
  • Churn risk
  • Expansion
  • Marketing mix
MOVES IT SCORES FIRST
01

Reprice the plan whose inference cost outruns its subscription

02

Meter the feature heavy users already treat as the product

03

Move spend to the channel whose signups reach production, not just signup

04

Catch the accounts churning on unproven ROI before their renewal, not after

Candidate moves, not commitments. Which ones rank for your company depends on what the engine reads when it connects.