Forecast AI evaluation production validity
Forecast whether offline AI evaluation scores will remain valid in production using a tenant-pooled Bayesian logit calibration with workload effects and time drift, coherent operating scenarios, false-promotion risk, breach timing, net value and quality-shortfall VaR/CVaR.
What it's for
Turns an offline model score into the executive question that matters: how likely is this route to disappoint in production, when, and with what economic downside?
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 |
|---|---|---|---|
| current_route_evaluations | array of objects (11 fields) | Evidence | Yes |
| historical_validation_cohorts | array of objects (9 fields) | Evidence | Yes |
| horizon_periods | integer ≥ 1, ≤ 120 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_current_evaluated_cases | integer ≥ 1 | Your calibration | Optional |
| minimum_evidence_coverage | number ≥ 0, ≤ 1 | Your calibration | Optional |
| minimum_historical_cohorts | integer ≥ 3 | Your calibration | Optional |
| minimum_workload_cohorts | integer ≥ 1 | Your calibration | Optional |
| prior_strength | number > 0 | Your calibration | Optional |
| scenarios | array of objects (7 fields) | Evidence | Yes |
| seed | integer ≥ 0 | Numerical control | Optional |
| simulations | integer ≥ 100, ≤ 100000 | Numerical control | Optional |
| tail_probability | number ≥ 0.001, ≤ 0.5 | Your calibration | Optional |
Each current_route_evaluations
record
| Field | Type | Required |
|---|---|---|
| evaluated_case_count | integer (≥ 1) | Yes |
| evaluation_score | number (≥ 0, ≤ 1) | Yes |
| evidence_verified | boolean | Yes |
| forecast_request_count_per_period | number (≥ 0) | Yes |
| id | string (non-empty) | Yes |
| inference_cost_per_request | number (≥ 0) | Yes |
| loss_per_failure | number (≥ 0) | Yes |
| minimum_production_quality | number (≥ 0, ≤ 1) | Yes |
| route_id | string (non-empty) | Yes |
| value_per_success | number (≥ 0) | Yes |
| workload_class | string (non-empty) | Yes |
{
"current_route_evaluations": [
{
"evaluated_case_count": 100,
"evaluation_score": 0.9,
"evidence_verified": true,
"forecast_request_count_per_period": 1000,
"id": "current-support",
"inference_cost_per_request": 0.05,
"loss_per_failure": 2,
"minimum_production_quality": 0.8,
"route_id": "route-support",
"value_per_success": 1,
"workload_class": "support"
},
{
"evaluated_case_count": 100,
"evaluation_score": 0.9,
"evidence_verified": true,
"forecast_request_count_per_period": 1000,
"id": "current-coding",
"inference_cost_per_request": 0.05,
"loss_per_failure": 2,
"minimum_production_quality": 0.8,
"route_id": "route-coding",
"value_per_success": 1,
"workload_class": "coding"
}
],
"historical_validation_cohorts": [
{
"cohort_period": 0,
"evaluated_case_count": 100,
"evaluation_score": 0.92,
"evidence_verified": true,
"id": "support-00",
"production_failure_count": 11,
"production_success_count": 89,
"transport_weight": 1,
"workload_class": "support"
},
{
"cohort_period": 1,
"evaluated_case_count": 100,Truncated for display — the full payload is 384 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.
{
"assumptions": [
"Historical cohorts pair a prospectively frozen evaluation with mature production outcomes under stable workload, score, outcome and transport-weight definitions; later information is excluded.",
"The tenant-pooled logit calibration relation and linear period drift are adequate over the declared horizon. Workload residuals partially pool locally and common scenarios preserve portfolio dependence.",
"Value per success, failure loss, inference cost, demand and quality thresholds are non-overlapping organization-owned quantities on one horizon and currency perimeter.",
"This is a conditional production-validity forecast, not causal business impact, a safety certification, vendor SLA, deployment or procurement approval, data-transfer authorization or judgment about a provider, team or person."
],
"calibration_model": {
"forecast_origin_period": 14,
"posterior_evaluation_slope_mean": 0.809,
"posterior_intercept_mean": -0.0881,
"posterior_period_drift_mean": -0.0148,
"residual_standard_deviation_logit_scale": 0.1
},
"configuration": {
"dependence_rule": "one_management_scenario_per_simulation_shared_across_every_route_and_period_with_one_workload_generalization_effect_shared_across_its_routes",
"horizon_periods": 6,
"minimum_current_evaluated_cases": 30,
"minimum_evidence_coverage": 0.95,
"minimum_historical_cohorts": 20,
"minimum_workload_cohorts": 5,
"prior_strength": 5,
"seed": 29,
"simulations": 200,
"tail_probability": 0.1
},
"decision": "ai_evaluation_production_validity_forecast_supported",
"failed_gates": [],
"method": "partially_pooled_bayesian_evaluation_to_production_validity_forecast_v1",
"route_diagnostics": [
{
"evaluated_case_count": 100,
"evaluation_score": 0.9,
"expected_first_breach_period_conditional": 2.061,
"expected_horizon_end_generalization_gap": -0.0931,
"expected_horizon_end_production_quality": 0.8069,
"expected_horizon_net_value": 1564.7601,
"expected_quality_shortfall_loss": 745.618,
"historical_workload_cohort_count": 15,
"probability_of_any_production_quality_breach": 0.41,
"probability_of_false_promotion": 0.41,
"route_id": "route-coding",
"supported": true,
"workload_class": "coding"Truncated for display — the full payload is 87 lines.
How it works
Sequential Bayesian & bandits — Learn while deciding — update beliefs as evidence arrives and choose where the next unit of effort is worth spending.
- 1 Join each frozen historical evaluation to mature production outcomes, transport weight, workload and cohort time; smooth bounded scores and fit a weighted Bayesian logit calibration with evaluation slope and period drift.
- 2 Partially pool workload generalization gaps, propagate current evaluation uncertainty, and share one management scenario across every route and horizon period in each simulation.
- 3 Report production-quality gaps, false-promotion and breach timing plus portfolio net value and quality-shortfall tail loss, abstaining when tenant or workload support and evidence coverage fail.
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
- The likelihood or reward model, prior support, action logging, delayed outcomes, and any stationarity assumptions match the deployment process.
- Historical evaluation-production pairs are prospective and mature, score/outcome/workload definitions are stable, transport weights target the current mix, and linear logit drift is adequate over the horizon.
- Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.
- Production-validity probability is conditional prediction—not causal business value, safety certification, vendor SLA, deployment/procurement/privacy/data-transfer approval or a judgment about a provider, team or person.
Minimum evidence
- historical_validation_cohorts: required and organization-defined
- current_route_evaluations: required and organization-defined
- scenarios: required and organization-defined
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
- immutable evaluation-to-production cohort projection joined on exact workload/model/evaluation/outcome versions, retaining failed and zero-traffic cohorts and estimating transport weights only from pre-deployment target-mix fields
- score and production-success equivalence, maturity/censoring, workload and cohort epoch, transport target and positivity, prior/support, forecast horizon, demand, quality SLO, inference cost, success value, failure loss, scenario dependence, currency and tail appetite
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": "forecast whether offline ai evaluation scores" }
→ finds "forecast_ai_evaluation_production_validity"
gitrevio_capability_describe
{ "capability_id": "forecast_ai_evaluation_production_validity" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_ai_evaluation_production_validity", "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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