CTM Business case update

studies/202512_GenesysCX/ctm-token-calculator/tests/test_cost_model.py

@@ -108,6 +108,50 @@ def test_consumption_tokens_rounded_up_monthly():
     assert df.iloc[0]["annual_cost"] == pytest.approx(3 * 12 * 1.0)
 
 
+def test_predictive_routing_consumption():
+    """1,700 calls/mo ÷ 17 per token = 100 tokens/mo → $1,200/yr (year 2, no ramp)."""
+    site = SiteInput(
+        "Tiny", "US", agents=5, supervisors=0,
+        voice_volume_monthly=1_700, email_volume_monthly=0,
+        chat_volume_monthly=0, sms_volume_monthly=0,
+        voice_aht_seconds=300, email_aht_seconds=600,
+        chat_aht_seconds=480, voice_acw_seconds=60,
+        fully_loaded_agent_cost_annual=65_000,
+        fully_loaded_supervisor_cost_annual=95_000,
+    )
+    df = calculate_consumption_ai_cost(
+        [site], FeatureScope("Predictive Routing", ["Tiny"]),
+        DEFAULT_METERS["Predictive Routing"], "realistic", DEFAULT_PRICING, year=2,
+    )
+    assert df.iloc[0]["tokens_monthly"] == 100
+    assert df.iloc[0]["annual_cost"] == pytest.approx(1_200)
+
+
+def test_predictive_routing_eligibility_and_total_cost():
+    """eligibility_pct halves the routed volume; total_cost handles the scope."""
+    site = SiteInput(
+        "Tiny", "US", agents=5, supervisors=0,
+        voice_volume_monthly=1_700, email_volume_monthly=0,
+        chat_volume_monthly=0, sms_volume_monthly=0,
+        voice_aht_seconds=300, email_aht_seconds=600,
+        chat_aht_seconds=480, voice_acw_seconds=60,
+        fully_loaded_agent_cost_annual=65_000,
+        fully_loaded_supervisor_cost_annual=95_000,
+    )
+    scope = FeatureScope("Predictive Routing", ["Tiny"], eligibility_pct=0.5)
+    df = calculate_consumption_ai_cost(
+        [site], scope, DEFAULT_METERS["Predictive Routing"], "realistic",
+        DEFAULT_PRICING, year=2,
+    )
+    assert df.iloc[0]["tokens_monthly"] == 50
+    total = calculate_total_cost(
+        [site], [scope], DEFAULT_METERS, DEFAULT_PRICING, "realistic", 2,
+        include_platform=False,
+    )
+    pr_row = total[total["cost_line"] == "Predictive Routing"].iloc[0]
+    assert pr_row["annual_cost"] == pytest.approx(50 * 12 * 1.0)
+
+
 def test_regional_pricing_not_hardcoded():
     pricing = dict(DEFAULT_PRICING)
     from tokencalc.meters import TokenPricing