Audit portfolio dependency value double counting

Reconcile project and dependency business-case claims to governed unique benefit sources under coherent scenarios, quantifying naive, unique, duplicated, and unassigned value before portfolio prioritization.

What it's for

Stops the same customer, revenue, risk-reduction or platform benefit from silently justifying several projects and dependency claims at once—before inflated ROI enters a board or investment decision.

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.

Field Type Role Required
allocations array of objects (5 fields) ≥ 1 item Evidence Yes
benefit_sources array of objects (2 fields) ≥ 1 item Evidence Yes
dependency_claims array of objects (3 fields) Evidence Yes
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_duplicate_fraction number ≥ 0, ≤ 1 Your calibration Optional
minimum_material_duplicate_value number ≥ 0 Your calibration Optional
projects array of objects (1 field) ≥ 1 item Evidence Yes
scenarios array of objects (2 fields) ≥ 2 items Evidence Yes

Each allocations record

Field Type Required
attribution_fraction number (> 0, ≤ 1) Yes
benefit_source_id string (non-empty) Yes
id string (non-empty) Yes
owner_id string (non-empty) Yes
owner_type one of "project", "dependency_claim" Yes
Example input
{
  "allocations": [
    {
      "attribution_fraction": 1,
      "benefit_source_id": "revenue",
      "id": "product-revenue",
      "owner_id": "product",
      "owner_type": "project"
    },
    {
      "attribution_fraction": 1,
      "benefit_source_id": "revenue",
      "id": "dependency-revenue",
      "owner_id": "platform-unblocks-product",
      "owner_type": "dependency_claim"
    },
    {
      "attribution_fraction": 1,
      "benefit_source_id": "cost-saving",
      "id": "platform-saving",
      "owner_id": "platform",
      "owner_type": "project"
    }
  ],
  "benefit_sources": [
    {
      "id": "revenue",
      "value_scenarios": [
        100,
        50
      ]
    },
    {
      "id": "cost-saving",
      "value_scenarios": [
        40,
        20
      ]
    }
  ],
  "dependency_claims": [
    {
      "downstream_project_id": "product",
      "id": "platform-unblocks-product",

Truncated for display — the full payload is 67 lines.

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.

Example output
{
  "assumptions": [
    "Every economic benefit has one stable governed source identity and one coherent scenario-value vector; projects and dependency claims reference attribution fractions of that same source rather than copying values under new labels.",
    "A source can contribute at most one full portfolio benefit. Fractions above one are duplicate attribution; fractions below one are visibly unassigned. This arithmetic does not decide which owner should receive credit or whether a dependency claim is causal.",
    "Source values share one counterfactual, horizon, currency, tax and probability basis and exclude transfers between portfolio entities; omitted sources, interactions, cannibalization, timing and costs can still make reconciled gross benefit differ from portfolio NPV.",
    "The audit applies to aggregate projects and claims, never named employees. Duplicate value triggers finance and portfolio reconciliation rather than blame, performance scoring, funding removal or automatic project cancellation."
  ],
  "benefit_source_diagnostics": [
    {
      "benefit_source_id": "revenue",
      "duplicate_attribution_fraction": 1,
      "expected_duplicate_value": 75,
      "expected_governed_source_value": 75,
      "total_attribution_fraction": 2,
      "unassigned_attribution_fraction": 0
    },
    {
      "benefit_source_id": "cost-saving",
      "duplicate_attribution_fraction": 0,
      "expected_duplicate_value": 0,
      "expected_governed_source_value": 30,
      "total_attribution_fraction": 1,
      "unassigned_attribution_fraction": 0
    }
  ],
  "configuration": {
    "maximum_duplicate_fraction": 0.05,
    "minimum_material_duplicate_value": 0,
    "scenario_alignment_preserved": true,
    "unique_source_cap": 1
  },
  "decision": "portfolio_dependency_value_double_counting_requires_reconciliation",
  "method": "unique_benefit_source_dependency_value_reconciliation_v1",
  "owner_diagnostics": [
    {
      "benefit_source_count": 1,
      "naive_expected_attributed_value": 75,
      "owner_id": "platform-unblocks-product",
      "owner_type": "dependency_claim"
    },
    {
      "benefit_source_count": 1,
      "naive_expected_attributed_value": 75,
      "owner_id": "product",

Truncated for display — the full payload is 89 lines.

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 Create one stable finance-governed identity and coherent scenario-value vector for every underlying benefit, then link project and dependency claims to fractional allocations of those identities rather than copied value fields.
  2. 2 Sum attribution fractions by benefit source, cap unique portfolio attribution at one, preserve fractions above one as duplicate value and fractions below one as unassigned value, and reconcile every amount under the same scenario probabilities.
  3. 3 Apply material-value and duplicate-fraction gates, return source, owner and scenario diagnostics, and require portfolio-finance reconciliation before downstream ROI, Pareto-frontier or funding analysis when either gate is breached.

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.

Assumptions & guardrails

  • Objectives use commensurable locally governed value units, constraints reflect real feasibility, and uncertainty covers plausible adverse inputs.
  • Benefit-source identity resolution is complete across direct projects and dependency claims; source values share one incremental counterfactual, horizon, currency, tax and probability basis; attribution fractions represent claimed economic ownership rather than delivery effort or individual credit.
  • The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
  • Duplicate attribution is an accounting and portfolio-model integrity signal, not proof of deception, causality, project worthlessness or poor performance, and it cannot trigger cancellation, compensation or employment action automatically.

Minimum evidence

  • benefit_sources: at least 1 rows/items
  • projects: at least 1 rows/items
  • dependency_claims: required and organization-defined
  • allocations: at least 1 rows/items
  • scenarios: at least 2 rows/items

How to validate it

Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

  • tenant-wide identity resolution across customer, revenue, cost, risk-reduction, platform and option-value claims, preserving one counterfactual, horizon, currency, tax basis and joint scenario order
  • portfolio and source perimeter, benefit definition, source matching policy, dependency-claim approval, allocation semantics, scenario probabilities, material duplicate value and fraction gates, finance ownership, and reconciliation cadence

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": "reconcile project and dependency businesscase claims" }
  → finds "audit_portfolio_dependency_value_double_counting"

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

gitrevio_capability_run
  { "capability_id": "audit_portfolio_dependency_value_double_counting", "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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