Audit analytics function calibration readiness

Gate analytical functions on paired out-of-time decision loss against a frozen baseline using temporal moving-block bootstrap, autocorrelation- and weight-adjusted effective sample size, evidence coverage, lower confidence bounds, improvement probability and recent degradation rather than declaring a model calibrated from training fit.

Statistical audit & measurement Audit CTOs Tech CEOs IT managers Team leads Investors

What it's for

Lets Gitrevio prove which analytics are calibrated for this company now, rather than asking customers to trust generic accuracy claims or another company's coefficients.

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
block_length	integer ≥ 1, ≤ 1000	Your calibration	Optional
bootstrap_draws	integer ≥ 200, ≤ 20000	Numerical control	Optional
confidence_level	number ≥ 0.5, < 1	Your calibration	Optional
evaluations	array of objects (7 fields) ≥ 4 items	Evidence	Yes
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
maximum_recent_improvement_degradation	number ≥ 0	Your calibration	Optional
minimum_effective_sample_size	number ≥ 1	Your calibration	Optional
minimum_evidence_coverage	number ≥ 0, ≤ 1	Your calibration	Optional
minimum_expected_loss_improvement	number	Your calibration	Optional
minimum_probability_loss_improvement	number ≥ 0, ≤ 1	Your calibration	Optional
minimum_resolved_cases	integer ≥ 2, ≤ 100000	Your calibration	Optional
recent_window_size	integer ≥ 2, ≤ 10000	Your calibration	Optional
seed	integer ≥ 0, ≤ 2147483647	Numerical control	Optional

Each `evaluations` record

Field	Type	Required
baseline_loss	number,null (≥ 0)	Optional
candidate_loss	number,null (≥ 0)	Optional
decision_period	integer (≥ 0, ≤ 10000000)	Yes
evidence_complete	boolean	Yes
function_id	string (non-empty)	Yes
id	string (non-empty)	Yes
weight	number (> 0)	Yes

Example input

{
  "bootstrap_draws": 200,
  "evaluations": [
    {
      "baseline_loss": 10,
      "candidate_loss": 8,
      "decision_period": 0,
      "evidence_complete": true,
      "function_id": "mcmc_project_completion_forecast",
      "id": "calibration-000",
      "weight": 1
    },
    {
      "baseline_loss": 10,
      "candidate_loss": 8,
      "decision_period": 1,
      "evidence_complete": true,
      "function_id": "mcmc_project_completion_forecast",
      "id": "calibration-001",
      "weight": 1
    },
    {
      "baseline_loss": 10,
      "candidate_loss": 8,
      "decision_period": 2,
      "evidence_complete": true,
      "function_id": "mcmc_project_completion_forecast",
      "id": "calibration-002",
      "weight": 1
    },
    {
      "baseline_loss": 10,
      "candidate_loss": 8,
      "decision_period": 3,
      "evidence_complete": true,
      "function_id": "mcmc_project_completion_forecast",
      "id": "calibration-003",
      "weight": 1
    },
    {
      "baseline_loss": 10,
      "candidate_loss": 8,
      "decision_period": 4,
      "evidence_complete": true,

Truncated for display — the full payload is 370 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

{
  "configuration": {
    "block_length": 5,
    "bootstrap_draws": 200,
    "confidence_level": 0.9,
    "maximum_recent_improvement_degradation": 0.1,
    "minimum_effective_sample_size": 20,
    "minimum_evidence_coverage": 0.9,
    "minimum_expected_loss_improvement": 1,
    "minimum_probability_loss_improvement": 0.9,
    "minimum_resolved_cases": 20,
    "seed": 7
  },
  "decision": "calibration_ready_functions_available",
  "function_diagnostics": [
    {
      "decision": "calibration_ready_for_governed_activation",
      "effective_sample_size": 40,
      "evidence_coverage": 1,
      "expected_loss_improvement": 2,
      "failed_gates": [],
      "function_id": "mcmc_project_completion_forecast",
      "lag_one_improvement_autocorrelation": 0,
      "loss_improvement_confidence_interval": [
        2,
        2
      ],
      "probability_loss_improvement": 1,
      "recent_improvement_degradation": 0,
      "recent_loss_improvement": 2,
      "resolved_case_count": 40,
      "submitted_case_count": 40
    }
  ],
  "guardrails": [
    "Candidate and baseline losses must be computed on the same prospectively resolved decisions with a frozen loss definition; training rows, unresolved cases and post-outcome feature reconstruction are not validation.",
    "Moving-block resampling preserves short temporal dependence, while the effective sample size penalizes autocorrelation and concentrated weights; neither repairs regime changes, selection bias or a missing counterfactual.",
    "Readiness is function-, organization-, outcome-, cadence- and epoch-specific; never transfer this result or fitted thresholds to another company without a transportability analysis.",
    "A ready result permits governed activation review, not autonomous deployment, and a failed result must not be reframed as employee, team or vendor underperformance."
  ],
  "method": "paired_temporal_block_bootstrap_calibration_readiness_v1",
  "summary": {
    "calibration_ready_function_count": 1,
    "calibration_ready_function_ids": [

Truncated for display — the full payload is 53 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 function identity, decision cadence, candidate and baseline loss definitions, evaluation cohort and point-in-time evidence completeness before outcomes resolve.
2 Within each function, sort resolved paired cases chronologically, estimate weighted loss improvement, penalize temporal autocorrelation and weight concentration, and moving-block-bootstrap its confidence interval and probability of clearing the practical gate.
3 Require coverage, resolved cases, effective sample size, lower-bound improvement, improvement probability and recent stability simultaneously before returning a locally calibrated activation candidate.

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.
Candidate and baseline losses are paired on the same prospectively resolved decisions; loss is proper and decision-relevant; rows are absent from training; cadence and block length reflect dependence; the recent window is a genuine deployment epoch.
Association, instability, or measurement quality is not a causal effect and must not be converted directly into an individual employment decision.
Readiness is function-, tenant-, outcome-, cadence- and epoch-specific; it is activation-review evidence rather than autonomous deployment authority and can never become a person or team score.

Minimum evidence

evaluations: at least 4 rows/items

How to validate it

Use chronological train/calibration/test windows, compare proper scores and decision value with a simple baseline, and recalibrate only from outcomes resolved after prediction time.

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 evaluation ledger joining frozen function version, baseline policy, decision-appropriate loss, holdout membership, prediction/action timestamp, later resolution, missingness and deployment epoch without training or outcome leakage
function/version/outcome/loss/baseline definitions, evaluation cohort and epoch, resolution and completeness policy, minimum cases/effective sample/coverage/improvement probability, dependence block, confidence, recent window/degradation and activation authority

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": "gate analytical functions on paired outoftime" }
  → finds "audit_analytics_function_calibration_readiness"

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

gitrevio_capability_run
  { "capability_id": "audit_analytics_function_calibration_readiness", "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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