Optimize delivery to cash intervention policy

What it's for

Allocates scarce engineering, customer-success and billing attention to the stage bottlenecks most likely to turn already-created technical value into cash without violating liquidity or downside limits.

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.

{
  "actions": [
    {
      "advance_probability_delta": 0.35,
      "capacity_units_per_milestone": 0.1,
      "cost_per_milestone": 0.05,
      "eligible_stages": [
        "delivery_ready",
        "accepted",
        "invoiced"
      ],
      "evidence_verified": true,
      "exit_probability_delta": -0.01,
      "fixed_cost": 1,
      "id": "expedite",
      "relationship_value_loss_per_milestone": 0
    }
  ],
  "current_unrestricted_cash": 100,
  "horizon_periods": 4,
  "intervention_budget": 100,
  "intervention_capacity": 100,
  "milestone_segments": [
    {
      "base_advance_probability": 0.25,
      "base_exit_probability": 0.03,
      "cash_value": 120,
      "evidence_verified": true,
      "id": "segment-delivery_ready",
      "milestone_count": 10,
      "stage": "delivery_ready"
    },
    {
      "base_advance_probability": 0.25,
      "base_exit_probability": 0.03,
      "cash_value": 100,
      "evidence_verified": true,
      "id": "segment-accepted",
      "milestone_count": 10,
      "stage": "accepted"
    },
    {
      "base_advance_probability": 0.25,
      "base_exit_probability": 0.03,

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.

{
  "constraints": {
    "failed": [],
    "feasible_policy_found": true
  },
  "decision": "delivery_to_cash_intervention_policy_supported",
  "guardrails": [
    "Interventions operate on lawful aggregate milestone segments, not named customers or individual workers.",
    "Action transition effects require randomized, quasi-experimental, or governed historical evidence.",
    "Beam search is explicitly uncertified; exact enumeration alone certifies the global optimum over supplied actions.",
    "The engine recommends analysis-ready policy; it does not issue invoices, alter contracts, or contact customers."
  ],
  "method": "aggregate_delivery_to_cash_markov_multiple_choice_cvar_policy_search",
  "scenario_diagnostics": [
    {
      "cash_collected": 210.9298,
      "minimum_unrestricted_cash": 95.5,
      "probability": 0.7,
      "scenario_id": "base",
      "terminal_net_value": 206.4298
    },
    {
      "cash_collected": 164.4774,
      "minimum_unrestricted_cash": 95.5,
      "probability": 0.3,
      "scenario_id": "slow",
      "terminal_net_value": 159.9774
    }
  ],
  "search": {
    "evaluated_policy_count": 8,
    "global_optimum_certificate": true,
    "policy_space_size": 8,
    "solver": "exact_enumeration"
  },
  "selected_actions": [
    {
      "action_id": "expedite",
      "segment_id": "segment-accepted",
      "stage": "accepted"
    },
    {
      "action_id": "expedite",
      "segment_id": "segment-delivery_ready",

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Freeze lawful aggregate milestone segments, executable stage-eligible actions, prospectively supported transition deltas, full costs/capacity/relationship impact, common scenarios and executive risk gates.
2. 2 Precompute each segment/action/scenario cash trajectory through delivery-ready, accepted, invoiced and paid states, applying fixed cash flow once per period and recovery only on governed invoice exit.
3. 3 Search one passive-or-action choice per segment, enforce all gates before ranking expected net value minus weighted CVaR, compare against all-passive policy, and disclose whether exact enumeration certifies the optimum.

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": "choose at most one evidencebacked intervention" }
  → finds "optimize_delivery_to_cash_intervention_policy"

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

gitrevio_capability_run
  { "capability_id": "optimize_delivery_to_cash_intervention_policy", "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 Finance & unit economics →