Audit value realization chain integrity
Audit one frozen aggregate cohort chain from eligible strategy scope through implementation, adoption, business outcome, monetization and cash collection, using whole-cohort bootstrap and simultaneous conversion, end-to-end, evidence and support gates.
What it's for
Creates an auditable strategy-to-cash control tower: leaders can see exactly where approved value stops progressing instead of confusing implementation, adoption, business impact and booked cash.
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 |
|---|---|---|---|
| bootstrap_draws | integer ≥ 200, ≤ 20000 | Numerical control | Optional |
| confidence_level | number ≥ 0.5, < 1 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_unverified_exposure_fraction | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_adoption_rate | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_collection_rate | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_end_to_end_conversion_rate | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_implementation_rate | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_mature_cohort_count | integer ≥ 5, ≤ 100000 | Your calibration | Optional |
| minimum_monetization_rate | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_outcome_rate | number ≥ 0, ≤ 1 | Your calibration | Optional |
| observations | array of objects (13 fields) ≥ 20 items | Evidence | Yes |
| seed | integer ≥ 0, ≤ 2147483647 | Numerical control | Optional |
Each observations
record
| Field | Type | Required |
|---|---|---|
| adopted_units | integer (≥ 0, ≤ 1000000000) | Yes |
| as_of_period | integer (≥ 0, ≤ 1000000000) | Yes |
| cohort_id | string (non-empty) | Yes |
| cohort_mature | boolean | Yes |
| collected_units | integer (≥ 0, ≤ 1000000000) | Yes |
| eligible_units | integer (≥ 1, ≤ 1000000000) | Yes |
| evidence_verified | boolean | Yes |
| id | string (non-empty) | Yes |
| implemented_units | integer (≥ 0, ≤ 1000000000) | Yes |
| initiative_id | string (non-empty) | Yes |
| monetized_units | integer (≥ 0, ≤ 1000000000) | Yes |
| outcome_units | integer (≥ 0, ≤ 1000000000) | Yes |
| weight | number (> 0) | Yes |
{
"bootstrap_draws": 200,
"confidence_level": 0.8,
"minimum_end_to_end_conversion_rate": 0.5,
"observations": [
{
"adopted_units": 80,
"as_of_period": 10,
"cohort_id": "cohort-00",
"cohort_mature": true,
"collected_units": 55,
"eligible_units": 100,
"evidence_verified": true,
"id": "platform-cohort-0",
"implemented_units": 90,
"initiative_id": "shared-platform",
"monetized_units": 60,
"outcome_units": 70,
"weight": 1
},
{
"adopted_units": 80,
"as_of_period": 10,
"cohort_id": "cohort-01",
"cohort_mature": true,
"collected_units": 55,
"eligible_units": 100,
"evidence_verified": true,
"id": "platform-cohort-1",
"implemented_units": 90,
"initiative_id": "shared-platform",
"monetized_units": 60,
"outcome_units": 70,
"weight": 1
},
{
"adopted_units": 80,
"as_of_period": 10,
"cohort_id": "cohort-02",
"cohort_mature": true,
"collected_units": 55,
"eligible_units": 100,
"evidence_verified": true,
"id": "platform-cohort-2",Truncated for display — the full payload is 307 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.
{
"configuration": {
"bonferroni_one_sided_tail_probability": 0.02857143,
"bootstrap_draws": 200,
"confidence_level": 0.8,
"maximum_unverified_exposure_fraction": 0.05,
"minimum_adoption_rate": 0.7,
"minimum_collection_rate": 0.8,
"minimum_end_to_end_conversion_rate": 0.5,
"minimum_implementation_rate": 0.8,
"minimum_mature_cohort_count": 10,
"minimum_monetization_rate": 0.7,
"minimum_outcome_rate": 0.7,
"seed": 0,
"simultaneous_gate_count": 7
},
"decision": "represented_value_realization_chains_supported",
"guardrails": [
"Every row is one frozen aggregate cohort carried through the same strategy-to-cash perimeter. Missing or zero-success cohorts remain in the denominator; immature cohorts remain visible but are not mislabeled as failures.",
"Whole-cohort bootstrap preserves dependence across the five nested conversion stages. Simultaneous bounds reduce false clearance across initiatives and gates but do not repair selective cohort creation, changed definitions or missing offline value paths.",
"The lowest conditional conversion is a bottleneck diagnostic, not causal attribution. This audit evaluates an aggregate value-realization system and must never become an employee score or a claim that activity caused revenue."
],
"initiative_diagnostics": [
{
"adoption_rate": 0.8889,
"adoption_rate_simultaneous_lower_bound": 0.8889,
"as_of_period": 10,
"bottleneck_stage": "monetization",
"cohort_count": 20,
"collection_rate": 0.9167,
"collection_rate_simultaneous_lower_bound": 0.9167,
"decision": "value_realization_chain_supported",
"end_to_end_conversion_rate": 0.55,
"end_to_end_conversion_rate_simultaneous_lower_bound": 0.55,
"failed_gates": [],
"immature_cohort_count": 0,
"implementation_rate": 0.9,
"implementation_rate_simultaneous_lower_bound": 0.9,
"initiative_id": "shared-platform",
"mature_cohort_count": 20,
"monetization_rate": 0.8571,
"monetization_rate_simultaneous_lower_bound": 0.8571,
"outcome_rate": 0.875,
"outcome_rate_simultaneous_lower_bound": 0.875,Truncated for display — the full payload is 59 lines.
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 Freeze each initiative's eligible aggregate cohorts and carry every mature cohort through nested implementation, adoption, outcome, monetization and collection counts without dropping zero-success paths.
- 2 Calculate conditional stage and end-to-end conversion plus exposure-weighted evidence verification while keeping immature cohorts visible but outside mature-outcome denominators.
- 3 Resample whole cohorts and apply family-wide one-sided Bonferroni bounds across initiatives and gates; identify the lowest conditional conversion as a diagnostic bottleneck only after the represented chain clears coherence checks.
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
- Metric definitions, weights, aggregate grain, sampling, missingness, dependence, and comparison windows correspond to the management claim being audited.
- Counts are nested for one immutable cohort/perimeter; eligibility and stage definitions do not change after outcomes; maturity and zero-success handling are complete; cohort dependence is compatible with aggregate bootstrap; collected value is not inferred from activity.
- Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
- A low stage conversion is a system bottleneck, not causal blame. Passing accounting integrity neither proves the strategy caused value nor authorizes individual performance, compensation or employment decisions.
Minimum evidence
- observations: at least 20 rows/items
How to validate it
Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.
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
- point-in-time strategy-to-cash cohort spine joining frozen eligible scope to implementation, qualifying adoption, mature causally governed outcome, finance monetization and cash collection while retaining every zero-success and immature cohort
- initiative/cohort perimeter, aggregate privacy grain, stage definitions and event time, outcome maturity, zero and churn handling, finance collection policy, evidence verification, weights, minimum support, conversion gates, simultaneous confidence and review owner
Calibration workflow
- 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
- 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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
- 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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
- 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 one frozen aggregate cohort chain" }
→ finds "audit_value_realization_chain_integrity"
gitrevio_capability_describe
{ "capability_id": "audit_value_realization_chain_integrity" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "audit_value_realization_chain_integrity", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.
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