mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-18 15:29:36 +00:00
47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""North-star and supporting metrics definitions."""
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from __future__ import annotations
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from typing import Any
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def north_star_metrics() -> dict[str, Any]:
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return {
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"primary": "weekly_qualified_opportunities_accepted_or_drafted",
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"secondary": "meetings_booked_post_approval",
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"guardrail": "blocked_high_risk_outreach_count",
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}
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def activation_metrics() -> dict[str, Any]:
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return {
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"time_to_first_brief_view_minutes": "target < 15",
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"time_to_first_opportunity_review": "target < 1 day",
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"first_approved_draft_hours": "target < 72h from signup",
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}
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def retention_metrics() -> dict[str, Any]:
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return {
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"weekly_active_brief": "WAU brief opens",
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"proof_pack_open_rate": "target > 60%",
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"expansion_trigger": "multi-seat or performance addon attach",
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}
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def revenue_metrics() -> dict[str, Any]:
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return {
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"mrr": "subscriptions + recurring performance (contracted)",
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"nrr": "expansion minus churn",
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"pipeline_influenced_sar": "attributed opportunities tracked in revenue memory",
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}
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def ai_quality_metrics() -> dict[str, Any]:
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return {
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"approval_rate": "drafts approved / drafts proposed",
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"blocked_action_rate": "guardrail stops / risky attempts",
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"arabic_tone_checks": "sampled human review weekly",
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"hallucination_checks": "grounding to project chunks + radar evidence",
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}
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