Audit executive technology reporting integrity

What it's for

Makes a Board Quarterly or investor technology pack inspectable: every headline metric and risk narrative traces to the frozen source, effective definition and independently reviewed arithmetic.

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.

{
  "as_of_ms": 12000,
  "metric_definitions": [
    {
      "definition_hash": "sha256:delivery-v2",
      "direction": "higher_better",
      "effective_from_ms": 0,
      "effective_until_ms": null,
      "evidence_verified": true,
      "id": "delivery",
      "practical_change_threshold": 1,
      "target_value": null
    }
  ],
  "metric_observations": [
    {
      "cycle_id": "board-q3",
      "definition_hash": "sha256:delivery-v2",
      "evidence_verified": true,
      "id": "delivery-q3",
      "metric_id": "delivery",
      "observed_at_ms": 9500,
      "period_end_ms": 9000,
      "period_start_ms": 7000,
      "recomputed_comparison_value": 10,
      "recomputed_current_value": 12,
      "reported_change": 2,
      "reported_current_value": 12,
      "source_snapshot_hash": "sha256:board-q3"
    }
  ],
  "narrative_claims": [
    {
      "assertion": "improved",
      "cycle_id": "board-q3",
      "evidence_verified": true,
      "id": "delivery-claim",
      "independently_reviewed": true,
      "management_assumption": false,
      "subject_id": "delivery",
      "subject_type": "metric"
    },
    {
      "assertion": "risk_reduced",

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": [
    "The board or executive owner declares the complete metric and risk scope before the cutoff; metric definitions, source snapshots, claims and review evidence are immutable and point-in-time.",
    "Narrative consistency is a mechanical reconciliation to governed direction, target and materiality rules; it does not infer causality, intent or strategic merit."
  ],
  "cycle_diagnostics": [
    {
      "cycle_id": "board-q3",
      "failed_gates": [],
      "integrity_pass": true,
      "reporting_cutoff_at_ms": 10000,
      "required_risks": 1,
      "scoped_metrics": 1
    }
  ],
  "decision": "executive_technology_pack_ready_for_governed_review",
  "failed_gates": [],
  "limitations": [
    "Passing establishes reporting-pack evidence integrity, not the truth of an omitted universe, audit assurance, securities-law compliance, fiduciary adequacy or authority to publish.",
    "The function must not infer deception, employee performance, legal fault or investor suitability from a failed reconciliation."
  ],
  "method": "point_in_time_executive_reporting_reconciliation_v1",
  "reporting_integrity_ready": true,
  "summary": {
    "cycles_passing": 1,
    "minimum_verified_evidence_fraction": 0.95,
    "reporting_cycles": 1,
    "required_risks": 1,
    "scoped_metrics": 1,
    "verified_evidence_fraction": 1
  },
  "truncated_detail_rows": 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 each reporting cutoff, source snapshot, complete metric/risk scope, effective metric definitions and narrative ledger before inspecting results.
2. 2 Recompute every reported level and change, reconcile definition and snapshot hashes, reject future evidence, and mechanically compare each narrative to governed direction, target and materiality rules.
3. 3 Require every scoped metric and risk, owner, independent review and evidence flag; return explicit repair gates rather than inferring intent or silently accepting omissions.

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 a frozen executive technology pack" }
  → finds "audit_executive_technology_reporting_integrity"

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

gitrevio_capability_run
  { "capability_id": "audit_executive_technology_reporting_integrity", "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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