CTM Calculators: Add PS billing milestones, Adjust Genesys RAMP

studies/202512_GenesysCX/ctm-token-calculator/tests/test_appendix4.py

@@ -41,6 +41,10 @@ def test_ramp_zeroes_year_one_licences():
                                             2028: 4_300_000.0}
     assert a4.licence_costs_by_year(0)[2026] == 4_300_000.0
     assert a4.licence_costs_by_year(18)[2027] == pytest.approx(4_300_000 * 6 / 12)
+    # The order form's ramp is 6 months — licences bill from July 2026.
+    assert a4.DEFAULT_RAMP_MONTHS == 6
+    assert a4.licence_costs_by_year() == {2026: 2_150_000.0, 2027: 4_300_000.0,
+                                          2028: 4_300_000.0}
 
 
 def test_current_state_run_off():
@@ -113,3 +117,21 @@ def test_contracted_overlays_verbatim():
     assert a4.TCO_VERBATIM["ccaas_annual"] == 4_300_000   # deck record intact
     assert a4.tco("current_annual") == a4.TCO_VERBATIM["current_annual"]
     assert a4.licence_costs_by_year(12, a4.tco("ccaas_annual"))[2027] == 3_200_000
+
+
+def test_sow_milestones_and_managed_services():
+    assert a4.PS_CONTRACTED_TOTAL == pytest.approx(2_025_446.48)
+    for m in a4.PS_MILESTONES:                            # amounts match the shares
+        assert m["amount"] == pytest.approx(m["share"] * a4.PS_CONTRACTED_TOTAL,
+                                            abs=0.01)
+    ps = a4.ps_costs_by_year(contracted=True)             # 50/50 across 2026-27
+    assert ps[2026] == pytest.approx(607_633.94 + 405_089.30 + 167_000)
+    assert ps[2027] == pytest.approx(607_633.94 + 405_089.30)
+    assert ps[2028] == 0.0
+    # The deck's verbatim year-1 lump stays intact for the as-pitched frame.
+    assert a4.ps_costs_by_year() == {2026: 2_567_000, 2027: 0.0, 2028: 0.0}
+    # Managed services bill from the month after MCX go-live (Sep 30 → Oct).
+    ms = a4.managed_services_by_year()
+    assert ms[2026] == pytest.approx(410_918.40 * 3 / 12)
+    assert ms[2027] == pytest.approx(410_918.40)
+    assert ms[2028] == pytest.approx(410_918.40)

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

@@ -47,11 +47,9 @@ def test_email_benefit_split():
         [site], FeatureScope("Email AI (Auto-Respond)", ["Small"]),
         "realistic", year=1,
     )
+    # Auto-Suggest is not a separate line — it lives inside Agent Copilot.
     lines = set(df["benefit_line"])
-    assert lines == {
-        "Email Auto-Respond (displaced handling)",
-        "Email Auto-Suggest (drafting time)",
-    }
+    assert lines == {"Email Auto-Respond (displaced handling)"}
     # auto-respond: 1,000×12 × 20% × 600s × 50% × $0.01 = $7,200
     respond = df[df["benefit_line"].str.contains("Respond")]["annual_value"].sum()
     assert respond == pytest.approx(7_200)

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

@@ -28,8 +28,8 @@ def test_all_spec_meters_present():
         "AI Translate",
         # Genesys Cloud Copilot
         "Genesys Cloud Copilot",
-        # Email AI (rates TBD)
-        "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)",
+        # Email AI (rate TBD; Auto-Suggest is inside Agent Copilot)
+        "Email AI (Auto-Respond)",
     }
     assert expected == set(DEFAULT_METERS)
 
@@ -59,9 +59,7 @@ def test_confirmed_rates():
 
 def test_unknown_meters_flagged():
     unknown = {f for f, m in DEFAULT_METERS.items() if m.confidence is Confidence.UNKNOWN}
-    assert unknown == {
-        "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)",
-    }
+    assert unknown == {"Email AI (Auto-Respond)"}
     assert Confidence.UNKNOWN.icon == "🔴"
     assert Confidence.CONFIRMED.icon == "🟢"
 

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

@@ -41,20 +41,46 @@ def test_runrate_saving_annual():
     assert mw.runrate_saving_annual(regions=["NA"]) == pytest.approx(3_000_000)
     assert mw.runrate_saving_annual(licence_annual=4_800_000,
                                     regions=[]) == pytest.approx(2_500_000)
+    # Contracted frame: managed services stay in the run-rate forever.
+    assert mw.runrate_saving_annual(
+        a4.tco("ccaas_annual"), managed_annual=a4.MANAGED_SERVICES_ANNUAL
+    ) == pytest.approx(6_589_081.60)
+
+
+def _default_wfm_benefits_by_year():
+    ben = mw.wfm_benefits_by_year(_default_benefit_rollout())
+    return {y: float(ben.loc[ben.year == y, "benefit"].sum()) for y in a4.YEARS}
 
 
 def test_breakeven_extrapolates_past_window():
+    # Deck frame: deck licence rate (6-month ramp), verbatim PS lump,
+    # no managed services.
     cs = a4.current_state_inputs(SITES)
     cur = a4.current_costs_by_year(cs)
     lic = a4.licence_costs_by_year()
     ps = a4.ps_costs_by_year()
     total = {y: cur[y] + lic[y] + ps[y] for y in a4.YEARS}
-    ben = mw.wfm_benefits_by_year(_default_benefit_rollout())
-    ben_y = {y: float(ben.loc[ben.year == y, "benefit"].sum()) for y in a4.YEARS}
-    inc, net = a4.case_flows(total, ben_y)
-    assert sum(net.values()) == pytest.approx(-1_553_000, abs=1_000)
+    inc, net = a4.case_flows(total, _default_wfm_benefits_by_year())
+    assert sum(net.values()) == pytest.approx(-3_703_000, abs=1_000)
     label = mw.runrate_breakeven_label(net, mw.runrate_saving_annual())
-    assert label == "40 months (~Apr 2029, extrapolated)"
+    assert label == "44 months (~Aug 2029, extrapolated)"
+
+
+def test_contracted_frame_with_sow_and_managed_services():
+    # Contracted frame: signed licence rate, SOW PS milestones, managed
+    # services from MCX go-live — the case the notebook leads with.
+    cs = a4.current_state_inputs(SITES)
+    cur = a4.current_costs_by_year(cs)
+    lic = a4.licence_costs_by_year(annual=a4.tco("ccaas_annual"))
+    ps = a4.ps_costs_by_year(contracted=True)
+    man = a4.managed_services_by_year()
+    total = {y: cur[y] + lic[y] + ps[y] + man[y] for y in a4.YEARS}
+    inc, net = a4.case_flows(total, _default_wfm_benefits_by_year())
+    assert sum(net.values()) == pytest.approx(-1_503_013, abs=1_000)
+    runrate = mw.runrate_saving_annual(
+        a4.tco("ccaas_annual"), managed_annual=a4.MANAGED_SERVICES_ANNUAL)
+    label = mw.runrate_breakeven_label(net, runrate)
+    assert label == "39 months (~Mar 2029, extrapolated)"
 
 
 def test_breakeven_defers_in_window_and_guards_zero_runrate():

studies/202512_GenesysCX/ctm-token-calculator/tokencalc/appendix4.py

@@ -107,6 +107,26 @@ TCO_CONTRACTED: dict[str, float] = {
     "ccaas_annual": 3_200_000,     # signed licence run-rate (deck pitched $4.3M/yr)
 }
 
+#: NTT professional services — SOW billing milestones (🟢 contractual).
+#: The deck's verbatim anchor books $2.4M PS in year 1; the signed SOW
+#: bills $2,025,446.48 in four milestones split 50/50 across 2026-27.
+PS_MILESTONES: list[dict] = [
+    {"name": "SOW Effective Date",                   "date": dt.date(2026, 3, 15),
+     "share": 0.30, "amount": 607_633.94},
+    {"name": "Start of client UAT (first region)",   "date": dt.date(2026, 9, 30),
+     "share": 0.20, "amount": 405_089.30},
+    {"name": "Start of client UAT (last region)",    "date": dt.date(2027, 6, 30),
+     "share": 0.30, "amount": 607_633.94},
+    {"name": "Completion of last go-live migration", "date": dt.date(2027, 9, 30),
+     "share": 0.20, "amount": 405_089.30},
+]
+PS_CONTRACTED_TOTAL = sum(m["amount"] for m in PS_MILESTONES)
+
+#: NTT managed services — commences billing at MCX go-live (🟢 contractual).
+#: Not in the deck's TCO at all; an ongoing run-rate cost thereafter.
+MANAGED_SERVICES_ANNUAL = 410_918.40
+MCX_GO_LIVE = dt.date(2026, 9, 30)
+
 
 def tco(key: str) -> float:
     """Contracted value where one exists, else the deck's verbatim anchor."""
@@ -120,7 +140,7 @@ REALIZE_MONTH = {r: m + BENEFIT_LAG_MONTHS for r, m in IMPL_MONTH.items()}
 #: NA Gantt exception: Email implemented Jan 2027, realizes Apr 2027.
 NA_EMAIL_IMPL_MONTH = 13
 
-DEFAULT_RAMP_MONTHS = 12                       # Genesys ramp programme
+DEFAULT_RAMP_MONTHS = 6                        # Genesys ramp programme (🟢 order form)
 DEFAULT_TERMINATION = dt.date(2027, 12, 31)    # current-platform term contracts
 
 # ── Region ⇄ site mapping ────────────────────────────────────────────
@@ -275,12 +295,42 @@ def licence_costs_by_year(
             for y in YEARS}
 
 
-def ps_costs_by_year() -> dict[int, float]:
-    """Base professional services + training — verbatim, year 1 only."""
-    return {2026: TCO_VERBATIM["prof_services_y1"] + TCO_VERBATIM["training_y1"],
+def ps_costs_by_year(contracted: bool = False) -> dict[int, float]:
+    """Base professional services + training.
+
+    Verbatim: the deck's $2.4M PS lump plus training, all in year 1.
+    Contracted: PS phased on the SOW billing milestones (50/50 across
+    2026-27, $2.03M total); training stays the verbatim year-1 line —
+    the SOW milestones don't itemize it separately.
+    """
+    training = TCO_VERBATIM["training_y1"]
+    if contracted:
+        ps = {y: 0.0 for y in YEARS}
+        for m in PS_MILESTONES:
+            ps[m["date"].year] += m["amount"]
+        return {y: ps[y] + (training if y == 2026 else 0.0) for y in YEARS}
+    return {2026: TCO_VERBATIM["prof_services_y1"] + training,
             2027: 0.0, 2028: 0.0}
 
 
+def ps_milestones_dataframe() -> pd.DataFrame:
+    """The SOW billing milestones as a display table."""
+    return pd.DataFrame(PS_MILESTONES)
+
+
+def managed_services_by_year(
+    annual: float = MANAGED_SERVICES_ANNUAL, start: dt.date = MCX_GO_LIVE
+) -> dict[int, float]:
+    """Managed services bill from the month after go-live (Sep 30 → Oct),
+    then run at the full annual rate — an ongoing cost with no end date
+    inside the model window."""
+    def _months(y: int) -> int:
+        if y < start.year:
+            return 0
+        return 12 if y > start.year else 12 - start.month
+    return {y: annual * _months(y) / 12 for y in YEARS}
+
+
 # ── Token consumption (missing cost #1) ──────────────────────────────
 
 
@@ -293,7 +343,6 @@ def claim_scenario(email_auto_respond_rate: float = 0.255) -> Scenario:
         voice_summarization_eligibility=0.0,
         voice_knowledge_eligibility=0.0,       # unused by the Appendix-4 scope set
         email_auto_respond_rate=email_auto_respond_rate,
-        email_auto_suggest_acceptance=0.0,     # Auto-Suggest is inside Copilot (V2 #1)
         consumption_cost_realization={1: 1.0, 2: 1.0, 3: 1.0},
     )
 

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)
 
 

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

@@ -86,8 +86,7 @@ def calculate_per_user_ai_cost(
     use_contracted: bool = False,
     rollout: RolloutPlan | None = None,
 ) -> pd.DataFrame:
-    """Per-user-per-month AI features (STA, Agent Copilot, AI Translate,
-    Email Auto-Suggest).
+    """Per-user-per-month AI features (STA, Agent Copilot, AI Translate).
 
     No adoption ramp and no rounding (users × tokens/user/month is
     exact) — but token usage only starts at site go-live, so the year

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

@@ -210,15 +210,9 @@ DEFAULT_METERS: dict[str, TokenMeter] = {
             ),
             source_url=_GENESYS_TOKEN_METERS,
         ),
-        # ── Email AI (rates not yet published) ────────────────────────
-        TokenMeter(
-            feature="Email AI (Auto-Suggest)",
-            meter_type=MeterType.PER_USER_PER_MONTH,
-            units_per_token=0.0,
-            tokens_per_unit=0.0,
-            confidence=Confidence.UNKNOWN,
-            notes="Requires Agent Copilot. Token rate not yet published.",
-        ),
+        # ── Email AI (rate not yet published) ─────────────────────────
+        # Email Auto-Suggest is included in Agent Copilot's per-user
+        # meter (no standalone SKU) — only Auto-Respond is listed here.
         TokenMeter(
             feature="Email AI (Auto-Respond)",
             meter_type=MeterType.PER_MESSAGE,
@@ -423,7 +417,6 @@ CTM_DEFAULT_FEATURE_SCOPES: list[FeatureScope] = [
     FeatureScope("AI Summary & Insights", ALL_SITE_NAMES, phase=1,
                  adoption_curve=_RAMP),
     FeatureScope("Direct Messaging", ALL_SITE_NAMES, phase=1, adoption_curve=_RAMP),
-    FeatureScope("Email AI (Auto-Suggest)", ["NAM", "EMEA"], phase=2),
     FeatureScope("AI Translate",
                  ["APAC HK", "APAC SG", "APAC SH", "APAC GZ", "APAC JP", "APAC TW"],
                  phase=3),

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

@@ -57,13 +57,18 @@ def runrate_saving_annual(
     licence_annual: float | None = None,
     regions: list[str] | None = None,
     baseline_annual: float | None = None,
+    managed_annual: float = 0.0,
 ) -> float:
     """Steady-state annual saving once term contracts end and the ramp
-    is over: (baseline − licence run-rate) + scoped WFM annual values.
+    is over: (baseline − licence run-rate − managed services) + scoped
+    WFM annual values.
+
+    Defaults are the deck frame (deck licence rate, no managed
+    services); the contracted frame passes ``a4.MANAGED_SERVICES_ANNUAL``.
     """
     lic = a4.TCO_VERBATIM["ccaas_annual"] if licence_annual is None else licence_annual
     base = a4.TCO_VERBATIM["current_annual"] if baseline_annual is None else baseline_annual
-    return (base - lic) + wfm_annual_runrate(regions)
+    return (base - lic - managed_annual) + wfm_annual_runrate(regions)
 
 
 def runrate_breakeven_label(

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

@@ -25,7 +25,6 @@ class Scenario:
     voice_summarization_eligibility: float
     voice_knowledge_eligibility: float
     email_auto_respond_rate: float       # share of email auto-responded
-    email_auto_suggest_acceptance: float
 
     # ── Virtual Agent benefit realization factors ───────────────────
     # Applied to both Voice Bot and Agentic VA deflection benefits.
@@ -75,7 +74,6 @@ BENEFIT_PARAMS: dict[str, dict[str, float]] = {
     "digital_aht_reduction": {"claim": 0.18, "realistic": 0.085},           # 5-12% Y1
     "digital_acw_reduction": {"claim": 1.00, "realistic": 0.40},            # 30-50% Y1
     "sta_aht_reduction": {"claim": 0.04, "realistic": 0.015},               # 1-2% Y1
-    "email_auto_suggest_time_saving": {"claim": 0.40, "realistic": 0.30},   # × acceptance; Genesys claims 40%
     # ESTIMATED lines (no Genesys claim published):
     "supervisor_copilot_time_saving": {"claim": 0.10, "realistic": 0.05},
     "predictive_routing_aht_reduction": {"claim": 0.04, "realistic": 0.02},
@@ -97,7 +95,6 @@ SCENARIOS: dict[str, Scenario] = {
         voice_summarization_eligibility=0.50,
         voice_knowledge_eligibility=0.40,
         email_auto_respond_rate=0.10,
-        email_auto_suggest_acceptance=0.25,
         # VA realization: conservative — low completion, limited staffing flex
         # Combined: 0.60 × 0.70 × (1 − 0.05) ≈ 0.40
         va_completion_rate=0.60,
@@ -113,7 +110,6 @@ SCENARIOS: dict[str, Scenario] = {
         voice_summarization_eligibility=0.70,
         voice_knowledge_eligibility=0.60,
         email_auto_respond_rate=0.20,
-        email_auto_suggest_acceptance=0.40,
         # VA realization: production midpoints per spec analysis
         # Combined: 0.70 × 0.80 × (1 − 0.05) ≈ 0.53
         va_completion_rate=0.70,
@@ -129,7 +125,6 @@ SCENARIOS: dict[str, Scenario] = {
         voice_summarization_eligibility=0.90,
         voice_knowledge_eligibility=0.80,
         email_auto_respond_rate=0.50,
-        email_auto_suggest_acceptance=0.60,
         # VA realization: optimistic — high completion, good staffing flexibility
         # Combined: 0.75 × 0.85 × (1 − 0.03) ≈ 0.62
         va_completion_rate=0.75,