Optimize value realization recovery portfolio

What it's for

Moves Gitrevio from describing a value leak to choosing the strongest recovery portfolio—recognizing that fixing adoption may be worthless until implementation works, and reporting exactly when the solver is heuristic.

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.

{
  "initiatives": [
    {
      "adoption_fraction_scenarios": [
        0.6,
        0.5
      ],
      "approved_value_scenarios": [
        100,
        80
      ],
      "collection_fraction_scenarios": [
        0.95,
        0.85
      ],
      "id": "shared-platform",
      "implementation_fraction_scenarios": [
        0.7,
        0.6
      ],
      "monetization_fraction_scenarios": [
        0.9,
        0.8
      ],
      "outcome_fraction_scenarios": [
        0.8,
        0.7
      ]
    }
  ],
  "interventions": [
    {
      "budget_cost": 3,
      "capacity_units": 1,
      "conditional_gap_closure_fraction_scenarios": [
        0.7,
        0.5
      ],
      "cost_scenarios": [
        3,
        4
      ],
      "dependency_ids": [],
      "exclusion_ids": [],

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.

{
  "configuration": {
    "maximum_acceptable_cvar_loss": null,
    "minimum_expected_net_value": 0,
    "minimum_probability_positive_net_value": 0.5,
    "recovery_budget": 5,
    "recovery_capacity_units": 2,
    "risk_aversion": 0,
    "scenario_count": 2,
    "tail_probability": 0.3
  },
  "decision": "value_realization_recovery_portfolio_supported",
  "guardrails": [
    "Conditional gap-closure fractions must be prospectively local effects at the declared initiative, stage, cadence and baseline; they are combined as diminishing closure of the remaining stage gap and never as additive percentage points.",
    "Common scenario columns preserve correlation across value, stage conversion, intervention effect and cost. Cross-stage improvements are strategically complementary because collected cash requires every stage; isolated ROI rankings can therefore be wrong.",
    "Exact mode certifies only the represented candidate set and scenarios. Beam mode is feasible but not globally optimal. Selection cannot override causal-evidence, legal, safety, liquidity, implementation-authority or aggregate privacy gates."
  ],
  "initiative_stage_diagnostics": [
    {
      "baseline_stage_rates": {
        "adoption": 0.57,
        "collection": 0.92,
        "implementation": 0.67,
        "monetization": 0.87,
        "outcome": 0.77
      },
      "expected_baseline_cash": 23.5368,
      "expected_selected_cash": 42.9969,
      "initiative_id": "shared-platform",
      "selected_stage_rates": {
        "adoption": 0.798,
        "collection": 0.92,
        "implementation": 0.877,
        "monetization": 0.87,
        "outcome": 0.77
      }
    }
  ],
  "method": "coherent_nonlinear_value_chain_gap_closure_portfolio_v1",
  "selected_interventions": [
    {
      "budget_cost": 2,
      "capacity_units": 1,
      "dependency_closed_leave_one_out_expected_net_value_loss": 9.9848,

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 each initiative's approved value and baseline five-stage conversion inside coherent scenarios, then define locally validated interventions by target stage, conditional remaining-gap closure, full scenario cost, budget/capacity use, dependencies and exclusions.
2. 2 For every feasible portfolio, combine same-stage actions as diminishing closure of the remaining gap and multiply the resulting stage conversions, preserving the complementarity that makes downstream recovery more valuable after upstream repair.
3. 3 Select the maximum expected-minus-risk-penalty net recovery value subject to positive-value and CVaR gates, returning an exact global certificate inside the state limit or an explicit dependency-closed beam-search fallback beyond it.

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 a budgeted capacityfeasible portfolio of" }
  → finds "optimize_value_realization_recovery_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_value_realization_recovery_portfolio", "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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