Robert Helewka
64fb83257d
- Add 202512_GenesysCX TEI study (config, seed data, notebooks, README) with NPV $10.8M / ROI 266% including AI-token cost line - Add explicit `key` parameter to all chart wrappers in app/components to prevent StreamlitDuplicateElementId errors when the same figure type renders across Summary/Benefits/Costs tabs - Render benefits bar and cost pie charts on their respective tabs - Add benefits_vs_costs_by_year chart wrapper
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
"""Benefit engine."""
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from __future__ import annotations
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import pytest
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from tokencalc.benefit_model import (
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calculate_acw_summarization_benefit,
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calculate_email_ai_benefit,
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calculate_total_benefit,
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)
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from tokencalc.defaults import CTM_DEFAULT_FEATURE_SCOPES, CTM_DEFAULT_SITES
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from tokencalc.inputs import WORKING_SECONDS_PER_YEAR, FeatureScope, SiteInput
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ALL_SITES = [s.site_name for s in CTM_DEFAULT_SITES]
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def _small_site() -> SiteInput:
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return SiteInput(
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"Small", "US", agents=10, supervisors=1,
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voice_volume_monthly=10_000, email_volume_monthly=1_000,
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chat_volume_monthly=0, sms_volume_monthly=0,
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voice_aht_seconds=300, email_aht_seconds=600,
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chat_aht_seconds=480, voice_acw_seconds=60,
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fully_loaded_agent_cost_annual=74_880, # → $0.01/second exactly
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fully_loaded_supervisor_cost_annual=95_000,
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)
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def test_acw_benefit_hand_check():
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"""10,000 calls × 12 × 70% eligible × 60s ACW × 40% reduction ×
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50% Y1 realization × $0.01/s = $10,080."""
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site = _small_site()
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assert site.agent_cost_per_second == pytest.approx(0.01)
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df = calculate_acw_summarization_benefit(
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[site], FeatureScope("Agent Copilot", ["Small"]), "realistic", year=1,
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)
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expected = 10_000 * 12 * 0.70 * 60 * 0.40 * 0.50 * 0.01
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assert df["annual_value"].sum() == pytest.approx(expected)
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def test_email_benefit_split():
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site = _small_site()
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df = calculate_email_ai_benefit(
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[site], FeatureScope("Email AI (Auto-Respond)", ["Small"]),
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"realistic", year=1,
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)
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lines = set(df["benefit_line"])
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assert lines == {
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"Email Auto-Respond (displaced handling)",
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"Email Auto-Suggest (drafting time)",
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}
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# auto-respond: 1,000×12 × 20% × 600s × 50% × $0.01 = $7,200
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respond = df[df["benefit_line"].str.contains("Respond")]["annual_value"].sum()
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assert respond == pytest.approx(7_200)
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def test_scenarios_produce_distinct_benefits():
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totals = {
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name: calculate_total_benefit(
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CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, name, year=2
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)["annual_value"].sum()
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for name in ("floor", "realistic", "stretch")
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}
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assert totals["floor"] < totals["realistic"] < totals["stretch"]
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def test_claim_exceeds_realistic():
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realistic = calculate_total_benefit(
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CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=1,
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params="realistic",
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)["annual_value"].sum()
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claim = calculate_total_benefit(
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CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=1,
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params="claim",
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)["annual_value"].sum()
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assert claim > realistic
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def test_benefits_ramp_by_year():
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by_year = [
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calculate_total_benefit(
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CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=y
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)["annual_value"].sum()
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for y in (1, 2, 3)
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]
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assert by_year[0] < by_year[1] < by_year[2]
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def test_zero_volume_site_is_safe():
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site = SiteInput(
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"Empty", "US", agents=0, supervisors=0,
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voice_volume_monthly=0, email_volume_monthly=0,
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chat_volume_monthly=0, sms_volume_monthly=0,
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voice_aht_seconds=300, email_aht_seconds=600,
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chat_aht_seconds=480, voice_acw_seconds=0,
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fully_loaded_agent_cost_annual=0,
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fully_loaded_supervisor_cost_annual=0,
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)
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df = calculate_total_benefit(
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[site], [FeatureScope("Agent Copilot", ["Empty"])], "realistic", year=1,
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)
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assert df["annual_value"].sum() == 0
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def test_working_seconds_constant():
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assert WORKING_SECONDS_PER_YEAR == 2_080 * 3_600
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