Forecast support cost to serve

What it's for

Forecasts how customer and product growth translate into support spend and budget risk, while refusing to validate a model that cannot beat a naive future baseline.

What you give it

Inputs split into evidence read from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "future_budget_per_period": [
    1700,
    1800
  ],
  "future_periods": [
    {
      "active_accounts": 230,
      "id": "next-1",
      "support_tickets": 115,
      "supported_products": 4
    },
    {
      "active_accounts": 240,
      "id": "next-2",
      "support_tickets": 120,
      "supported_products": 4
    }
  ],
  "historical_periods": [
    {
      "active_accounts": 100,
      "id": "period-0",
      "support_cost": 1000,
      "support_tickets": 50,
      "supported_products": 2
    },
    {
      "active_accounts": 110,
      "id": "period-1",
      "support_cost": 1040,
      "support_tickets": 55,
      "supported_products": 2
    },
    {
      "active_accounts": 120,
      "id": "period-2",
      "support_cost": 1080,
      "support_tickets": 60,
      "supported_products": 2
    },
    {
      "active_accounts": 130,
      "id": "period-3",

What you get back

This is the actual output of running the example above — computed by the same function the platform calls, not an illustration.

{
  "assumptions": [
    "Historical cost, accounts, supported-product count, and tickets use stable definitions, accounting treatment, cadence, and point-in-time availability across train, holdout, and forecast periods.",
    "Log-linear effects and residual distribution remain transportable into the future plan; planned automation, product mix, severity, channels, wage rates, and service levels are encoded in drivers or separately stressed.",
    "A failed holdout gate is an abstention even when the future interval looks precise; budget breach is conditional on the submitted operating plan."
  ],
  "configuration": {
    "confidence_level": 0.9,
    "seed": 43,
    "simulation_draws": 1000
  },
  "decision": "support_cost_forecast_validated",
  "method": "heldout_lognormal_support_cost_forecast_v1",
  "period_forecasts": [
    {
      "budget": 1700,
      "expected_cost_per_active_account": 6.5796,
      "expected_support_cost": 1513.3028,
      "period_id": "next-1",
      "probability_budget_breach": 0,
      "support_cost_interval": [
        1512.7796,
        1513.8403
      ]
    },
    {
      "budget": 1800,
      "expected_cost_per_active_account": 6.4633,
      "expected_support_cost": 1551.1999,
      "period_id": "next-2",
      "probability_budget_breach": 0,
      "support_cost_interval": [
        1550.6808,
        1551.7464
      ]
    }
  ],
  "summary": {
    "expected_total_future_support_cost": 3064.5027,
    "future_period_count": 2,
    "historical_period_count": 12,
    "maximum_period_budget_breach_probability": 0,
    "total_support_cost_interval": [
      3063.7753,

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1. 1 Freeze stable point-in-time definitions for support cost, active accounts, supported products, tickets, cadence, and future plan drivers, preserving chronological order.
2. 2 Fit log cost on the training prefix, estimate residual dispersion, and compare future-held-out MAE against a last-observation naive baseline before permitting a validated forecast decision.
3. 3 Simulate lognormal future cost paths and budget breach only after the gate; if the model fails, treat all numeric intervals as diagnostic abstention and investigate mix, automation, severity, wage, or service-level shifts.

Before you trust it

Every tool in the catalog ships with the conditions under which its answer is meaningful — and the conditions under which it should abstain instead of guessing.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 2 Build a tenant-scoped historical cohort using only information available before each prediction or decision; preserve zero periods, censoring, assignment probabilities, and unresolved outcomes when the method requires them.
3. 3 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
4. 4 Validate on later time windows or held-out aggregate units at the deployment grain, against a simple baseline and the function-specific validation strategy.
5. 5 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
6. 6 Monitor realized outcomes, data drift, coverage, and decision regret; recalibrate at a governed cadence or after a detected regime/definition change, never merely because a stakeholder dislikes the result.

Call it from your AI

You don't wire up 388 tools in your MCP client. The GitRevio MCP server exposes 18 tools, three of which let an agent search the catalog, read a tool's schema, and run it — so the assistant finds this one on its own.

gitrevio_capabilities_search
  { "q": "forecast future support cost and budgetbreach" }
  → finds "forecast_support_cost_to_serve"

gitrevio_capability_describe
  { "capability_id": "forecast_support_cost_to_serve" }
  → returns the input schema and agent guidance shown on this page

gitrevio_capability_run
  { "capability_id": "forecast_support_cost_to_serve", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

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