Forecast contract delivery and liability

What it's for

Turns commercial promises into an age-conditioned delivery, penalty and cash distribution, giving boards and operators a defensible view of liability before deadlines fail.

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.

{
  "current_commitments": [
    {
      "age_periods": 6,
      "baseline_planned_duration_periods": 10,
      "cash_on_acceptance": 100,
      "commitment_class": "standard",
      "evidence_verified": true,
      "id": "current-standard",
      "maximum_penalty": 50,
      "penalty_free_grace_periods": 1,
      "penalty_per_late_period": 10,
      "promised_total_duration_periods": 12
    }
  ],
  "current_unrestricted_cash": 100,
  "historical_commitment_episodes": [
    {
      "baseline_planned_duration_periods": 10,
      "commitment_class": "standard",
      "evidence_verified": true,
      "id": "standard-0",
      "observed_duration_periods": 8,
      "outcome": "censored"
    },
    {
      "baseline_planned_duration_periods": 10,
      "commitment_class": "standard",
      "evidence_verified": true,
      "id": "standard-1",
      "observed_duration_periods": 9,
      "outcome": "delivered"
    },
    {
      "baseline_planned_duration_periods": 10,
      "commitment_class": "standard",
      "evidence_verified": true,
      "id": "standard-2",
      "observed_duration_periods": 10,
      "outcome": "delivered"
    },
    {
      "baseline_planned_duration_periods": 10,
      "commitment_class": "standard",

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.

{
  "commitment_diagnostics": [
    {
      "commitment_class": "standard",
      "commitment_id": "current-standard",
      "contractual_penalty": {
        "mean": 11.9976,
        "p10": 0,
        "p50": 0,
        "p90": 48.2719
      },
      "delivery_within_horizon_probability": 0.93,
      "historical_support": {
        "censored": 3,
        "delivered": 7,
        "episodes": 10
      },
      "on_time_probability": 0.54,
      "used_pooled_fallback": false
    }
  ],
  "configuration": {
    "horizon_periods": 12,
    "mu_grid_points": 7,
    "prior_strength": 2,
    "scenario_probability_sum_before_normalization": 1,
    "seed": 9,
    "sigma_grid_points": 5,
    "simulations": 100
  },
  "decision": "contract_delivery_liability_forecast_supported",
  "forecast": {
    "acceptance_cash_within_horizon": {
      "mean": 93,
      "p10": 100,
      "p50": 100,
      "p90": 100
    },
    "contractual_penalty": {
      "mean": 11.9976,
      "p10": 0,
      "p50": 0,
      "p90": 48.2719
    },

How it works

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

1. 1 Normalize delivered and honestly censored historical durations by their point-in-time baseline plan and fit a lognormal likelihood in which unresolved episodes contribute survival probability rather than a fabricated late outcome.
2. 2 Partially pool commitment-class location and scale grids toward the tenant-wide distribution, retain class support, and draw each current total duration conditional on having already survived to its observed age.
3. 3 Apply coherent common duration, penalty and fixed-cash scenarios; calculate delivery/on-time events, governed penalty caps, acceptance cash and chronological minimum liquidity; abstain on sparse classes or failed evidence.

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 remaining commercialcommitment delivery time ontime" }
  → finds "forecast_contract_delivery_and_liability"

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

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

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

Related tools

See every tool in Delivery forecasting & commitments →