CTM Calculators: Add PS billing milestones, Adjust Genesys RAMP

studies/202512_GenesysCX/ctm-token-calculator/tokencalc/benefit_model.py

@@ -126,11 +126,12 @@ def calculate_email_ai_benefit(
     params: str = "realistic",
     rollout: RolloutPlan | None = None,
 ) -> pd.DataFrame:
-    """Email Auto-Respond (full displacement at the respond rate) plus
-    Auto-Suggest (time saving × acceptance on the remainder)."""
+    """Email Auto-Respond — full displacement at the respond rate.
+
+    (Email Auto-Suggest is not a separate benefit line: it is included
+    in Agent Copilot, whose per-user meter carries the drafting help.)"""
     sc = get_scenario(scenario) if isinstance(scenario, str) else scenario
     ro = rollout or NO_ROLLOUT
-    suggest_saving = _param("email_auto_suggest_time_saving", params)
     realization = sc.realization(year)
     rows = []
     for s in sites:
@@ -145,11 +146,6 @@ def calculate_email_ai_benefit(
         respond_seconds = (
             annual_emails * respond_rate * s.email_aht_seconds * realization
         )
-        suggest_seconds = (
-            annual_emails * (1 - respond_rate)
-            * sc.email_auto_suggest_acceptance * s.email_aht_seconds
-            * suggest_saving * realization
-        )
         rate = s.agent_cost_per_second
         rows.append(
             {
@@ -160,15 +156,6 @@ def calculate_email_ai_benefit(
                 "confidence": Confidence.UNKNOWN.value,  # meter rate unsourced
             }
         )
-        rows.append(
-            {
-                "benefit_line": "Email Auto-Suggest (drafting time)",
-                "scope": s.site_name,
-                "annual_value": suggest_seconds * rate
-                * ro.fraction_live(s.site_name, year),
-                "confidence": Confidence.UNKNOWN.value,
-            }
-        )
     return _df(rows)