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Recommendation examples

Product-real examples showing different recommendation scenarios, outcomes, and version changes.

chat_message underpriced due to margin compression

Feature
chat_message
Current
4 credits
Suggested
5 credits
Reason
Margin below target threshold (18% vs 25% target)
Confidence
High
Estimated monthly lift
+$182
Outcome
Applied
Version
v1 → v2

image.generate after provider cost drift

Feature
image.generate
Current
35 credits
Suggested
42 credits
Reason
Provider cost increased ~18%. Current pricing no longer covers cost.
Confidence
High
Estimated monthly lift
+$340
Outcome
Applied
Version
v2 → v3

agent.run due to high compute profile

Feature
agent.run
Current
20 credits
Suggested
28 credits
Reason
High compute profile (avg 3 tool calls, 2K context). Current credits under-recover.
Confidence
Medium
Estimated monthly lift
+$95
Outcome
Applied
Version
v1 → v2

doc.analyze rejected by operator

Feature
doc.analyze
Current
12 credits
Suggested
16 credits
Reason
Margin below target. Suggested increase to align with competitor pricing.
Confidence
Medium
Estimated monthly lift
+$48
Outcome
Rejected
Version

Operator kept v1. Rejection recorded in history.

Low-confidence recommendation left unchanged

Feature
workflow.step
Current
6 credits
Suggested
8 credits
Reason
Insufficient usage data. Estimate based on similar events only.
Confidence
Low
Estimated monthly lift
~$12 (uncertain)
Outcome
Not applied
Version

Left unchanged. Will re-evaluate when more data available.

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