mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
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202 lines
8.1 KiB
Python
202 lines
8.1 KiB
Python
"""
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Client Acquisition Simulator — pre-purchase ROI calculator.
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A prospect enters their sector / city / deal size / target / current close rate,
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and gets back an honest projection: how many leads they need, how many
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meetings, how many months to hit their goal, recommended plan, expected ROI.
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Used on /landing/simulator.html and inside onboarding to set realistic
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expectations.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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# ── Sector benchmark constants — anchored from Pulse data ─────────
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SECTOR_BENCHMARKS: dict[str, dict[str, float]] = {
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"real_estate": {"reply_rate": 0.074, "meeting_rate": 0.32, "win_rate": 0.18, "cycle_days": 45},
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"clinics": {"reply_rate": 0.138, "meeting_rate": 0.40, "win_rate": 0.28, "cycle_days": 28},
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"logistics": {"reply_rate": 0.068, "meeting_rate": 0.30, "win_rate": 0.22, "cycle_days": 35},
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"hospitality": {"reply_rate": 0.124, "meeting_rate": 0.38, "win_rate": 0.24, "cycle_days": 30},
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"restaurants": {"reply_rate": 0.115, "meeting_rate": 0.42, "win_rate": 0.30, "cycle_days": 21},
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"training": {"reply_rate": 0.112, "meeting_rate": 0.36, "win_rate": 0.25, "cycle_days": 35},
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"agencies": {"reply_rate": 0.059, "meeting_rate": 0.28, "win_rate": 0.20, "cycle_days": 45},
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"construction": {"reply_rate": 0.032, "meeting_rate": 0.25, "win_rate": 0.15, "cycle_days": 90},
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"saas": {"reply_rate": 0.047, "meeting_rate": 0.30, "win_rate": 0.20, "cycle_days": 60},
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"events": {"reply_rate": 0.153, "meeting_rate": 0.45, "win_rate": 0.35, "cycle_days": 18},
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}
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# Multiplier for Dealix lift (validated from pilot data)
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DEALIX_LIFT_MULTIPLIERS: dict[str, float] = {
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"reply_rate": 2.4,
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"meeting_rate": 1.4,
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"win_rate": 1.2,
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}
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@dataclass
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class SimulatorInputs:
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sector: str
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city: str
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avg_deal_value_sar: float
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target_revenue_sar: float
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target_period_days: int = 90
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current_close_rate: float | None = None # if known
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current_monthly_meetings: int = 0
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@dataclass
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class FunnelProjection:
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"""Forward-projected funnel for the requested period."""
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leads_needed: int
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replies_expected: int
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meetings_expected: int
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proposals_expected: int
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deals_won_expected: int
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revenue_expected_sar: float
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cycle_days_avg: float
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confidence_band_low: float
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confidence_band_high: float
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@dataclass
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class PlanRecommendation:
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plan_name: str # Starter / Growth / Scale
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monthly_price_sar: float
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fits_target: bool
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expected_payback_months: float
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rationale_ar: str
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@dataclass
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class SimulatorResult:
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inputs: SimulatorInputs
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baseline: FunnelProjection
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with_dealix: FunnelProjection
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plan: PlanRecommendation
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expected_roi_x: float
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risks_ar: list[str] = field(default_factory=list)
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assumptions_ar: list[str] = field(default_factory=list)
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def _compute_funnel(
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*,
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inputs: SimulatorInputs,
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bench: dict[str, float],
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with_dealix: bool,
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) -> FunnelProjection:
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reply = bench["reply_rate"] * (DEALIX_LIFT_MULTIPLIERS["reply_rate"] if with_dealix else 1.0)
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meet = bench["meeting_rate"] * (DEALIX_LIFT_MULTIPLIERS["meeting_rate"] if with_dealix else 1.0)
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win = bench["win_rate"] * (DEALIX_LIFT_MULTIPLIERS["win_rate"] if with_dealix else 1.0)
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deals_needed = max(1, round(inputs.target_revenue_sar / inputs.avg_deal_value_sar))
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proposals_needed = round(deals_needed / max(0.05, win))
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meetings_needed = round(proposals_needed / max(0.10, meet))
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replies_needed = round(meetings_needed * 1.6)
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leads_needed = round(replies_needed / max(0.005, reply))
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band = 0.25 # ±25% confidence interval
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revenue_expected = deals_needed * inputs.avg_deal_value_sar
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return FunnelProjection(
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leads_needed=leads_needed,
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replies_expected=replies_needed,
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meetings_expected=meetings_needed,
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proposals_expected=proposals_needed,
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deals_won_expected=deals_needed,
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revenue_expected_sar=revenue_expected,
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cycle_days_avg=bench["cycle_days"] * (0.8 if with_dealix else 1.0),
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confidence_band_low=revenue_expected * (1 - band),
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confidence_band_high=revenue_expected * (1 + band),
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)
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def _recommend_plan(*, inputs: SimulatorInputs, with_dealix: FunnelProjection) -> PlanRecommendation:
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monthly_revenue = with_dealix.revenue_expected_sar / max(1, inputs.target_period_days / 30)
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if inputs.avg_deal_value_sar < 5000 or with_dealix.leads_needed < 200:
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plan = PlanRecommendation(
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plan_name="Starter",
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monthly_price_sar=999,
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fits_target=monthly_revenue > 999 * 3,
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expected_payback_months=999 / max(1, monthly_revenue) * 12,
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rationale_ar=(
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"حجم الصفقات وعدد الـ leads المطلوبة يناسب باقة Starter — "
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"تبدأ بسرعة + ترفّع لاحقاً."
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),
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)
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elif inputs.avg_deal_value_sar < 50000 or with_dealix.leads_needed < 1000:
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plan = PlanRecommendation(
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plan_name="Growth",
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monthly_price_sar=2999,
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fits_target=monthly_revenue > 2999 * 3,
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expected_payback_months=2999 / max(1, monthly_revenue) * 12,
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rationale_ar=(
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"الباقة الأنسب — autopilot discovery + WhatsApp chain + "
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"AI personalization + monthly proof pack."
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),
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)
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else:
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plan = PlanRecommendation(
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plan_name="Scale",
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monthly_price_sar=7999,
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fits_target=monthly_revenue > 7999 * 3,
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expected_payback_months=7999 / max(1, monthly_revenue) * 12,
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rationale_ar=(
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"صفقات كبيرة + multi-sector + integrations + customer success — "
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"Scale هو المسار الصحيح."
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),
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)
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return plan
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def simulate(*, inputs: SimulatorInputs) -> SimulatorResult:
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"""Run the full simulation and return everything for the result page."""
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bench = SECTOR_BENCHMARKS.get(
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inputs.sector,
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# Default = SaaS-ish moderate benchmark
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{"reply_rate": 0.05, "meeting_rate": 0.30, "win_rate": 0.20, "cycle_days": 45},
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)
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if inputs.current_close_rate is not None:
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bench = dict(bench)
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bench["win_rate"] = inputs.current_close_rate
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baseline = _compute_funnel(inputs=inputs, bench=bench, with_dealix=False)
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with_dx = _compute_funnel(inputs=inputs, bench=bench, with_dealix=True)
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plan = _recommend_plan(inputs=inputs, with_dealix=with_dx)
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# ROI: revenue achieved with Dealix vs cost of Dealix.
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# Both funnels produce the same deal count (since deals_needed is derived
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# from the target), so we measure ROI as full-revenue / total-cost — i.e.,
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# how many multiples of the Dealix plan does the achieved revenue cover.
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months = max(1.0, inputs.target_period_days / 30)
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cost = plan.monthly_price_sar * months
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roi_x = round(with_dx.revenue_expected_sar / cost, 2) if cost else 0
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risks = []
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if inputs.target_period_days < 30:
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risks.append("الفترة قصيرة جداً (<30 يوم) — توقع رؤية النتائج بعد 45-60 يوم.")
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if inputs.avg_deal_value_sar < 1000:
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risks.append("صفقة بأقل من 1,000 ريال — تأكد من unit economics قبل الاستثمار.")
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if with_dx.leads_needed > 5000:
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risks.append(f"تحتاج {with_dx.leads_needed:,} lead — ابني capacity الفريق أولاً.")
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assumptions = [
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f"benchmark القطاع ({inputs.sector}) من Saudi B2B Pulse — ربع سنوي.",
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"Dealix lift متوسط 2.4× في الـ reply rate (مبني على pilot data).",
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f"متوسط الدورة: {with_dx.cycle_days_avg:.0f} يوم — قد تختلف حسب حجم الشركة.",
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"الأرقام indicative — ليست ضمان قانوني.",
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]
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return SimulatorResult(
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inputs=inputs,
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baseline=baseline,
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with_dealix=with_dx,
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plan=plan,
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expected_roi_x=roi_x,
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risks_ar=risks,
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assumptions_ar=assumptions,
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)
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