Audit cost allocation consistency

What it's for

Makes engineering unit economics auditable by showing whether shared platform, cloud, and vendor costs were allocated consistently before teams or products are compared.

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.

{
  "cost_pools": [
    {
      "allocation_basis": "governed-consumption",
      "allocations": [
        {
          "allocated_amount": 600,
          "basis_value": 3,
          "target_id": "product-a"
        },
        {
          "allocated_amount": 400,
          "basis_value": 2,
          "target_id": "product-b"
        }
      ],
      "id": "shared-platform",
      "source_amount": 1000
    }
  ]
}

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": [
    "Each pool has one complete source amount, one governed causal or consumption allocation basis, and non-overlapping target rows.",
    "Pro-rata arithmetic tests consistency with the declared basis; it does not validate whether that basis is economically appropriate.",
    "Currency, accounting period, capitalization, and intercompany elimination policies are consistent across source and target amounts."
  ],
  "configuration": {
    "maximum_pool_relative_error": 0.01,
    "maximum_target_relative_error": 0.02
  },
  "decision": "cost_allocation_consistency_supported",
  "method": "pro_rata_cost_allocation_consistency_audit_v1",
  "pool_diagnostics": [
    {
      "allocated_amount": 1000,
      "allocation_basis": "governed-consumption",
      "allocation_consistent": true,
      "failing_target_ids": [],
      "maximum_target_relative_error": 0,
      "pool_id": "shared-platform",
      "pool_relative_error": 0,
      "source_amount": 1000
    }
  ],
  "summary": {
    "aggregate_allocation_gap": 0,
    "cost_pool_count": 1,
    "failing_pool_count": 0,
    "total_allocated_cost": 1000,
    "total_source_cost": 1000
  },
  "truncated_detail_count": 0
}

How it works

Statistical audit & measurement — Check whether a number is fit to decide on: coverage, timing, reconciliation, and the gaps a dashboard hides.

1. 1 Freeze source pools, accounting period/currency/capitalization, target universe, one allocation basis per pool, and pool/target tolerances.
2. 2 Recompute each target's pro-rata allocation from basis share, reconcile allocated totals to source cost, and fail positive pools with zero basis or duplicate targets.
3. 3 Expose pool-level and target-level discrepancies separately so downstream unit economics can abstain before inconsistent allocations distort comparison.

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": "audit whether shared engineering platform cloud" }
  → finds "audit_cost_allocation_consistency"

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

gitrevio_capability_run
  { "capability_id": "audit_cost_allocation_consistency", "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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