Forecast AI knowledge staleness loss

Forecast stale and unsupported AI answers plus economic-loss VaR/CVaR by learning tenant-local knowledge-change hazards, retrieval failure and lognormal stale-loss severity, then simulating scheduled refreshes under coherent demand/change/loss scenarios and a shared index-failure state.

Forecasting & survival Forecast CTOs Tech CEOs IT managers Team leads Investors

What it's for

Prices the hidden liability behind a RAG dashboard: how often answers are likely to be stale or unsupported, what that costs, and how ugly the correlated downside becomes when the index itself fails.

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_domains	array of objects (13 fields)	Evidence	Yes
domain_history	array of objects (12 fields)	Evidence	Yes
horizon_periods	integer ≥ 1, ≤ 60	Your calibration	Optional
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
maximum_stale_answer_fraction	number ≥ 0, ≤ 1	Your calibration	Optional
minimum_history_periods	integer ≥ 1, ≤ 120	Your calibration	Optional
period_days	integer ≥ 1, ≤ 365	Your calibration	Optional
posterior_draws	integer ≥ 200, ≤ 100000	Numerical control	Optional
scenarios	array of objects (8 fields)	Evidence	Yes
seed	integer ≥ 0	Numerical control	Optional
tail_probability	number > 0, ≤ 0.5	Your calibration	Optional

Each `current_domains` record

Field	Type	Required
change_hazard_prior_rate_days	number (> 0)	Yes
change_hazard_prior_shape	number (> 0)	Yes
days_until_scheduled_refresh	integer (≥ 0)	Yes
evidence_verified	boolean	Yes
expected_queries_per_period	number (≥ 0)	Yes
id	string (non-empty)	Yes
refresh_interval_days	integer (≥ 1, ≤ 3650)	Yes
retrieval_failure_prior_alpha	number (> 0)	Yes
retrieval_failure_prior_beta	number (> 0)	Yes
source_age_days	number (≥ 0)	Yes
stale_loss_prior_log_mean	number	Yes
stale_loss_prior_log_sd	number (> 0)	Yes
value_per_grounded_answer	number (≥ 0)	Yes

Example input

{
  "current_domains": [
    {
      "change_hazard_prior_rate_days": 365,
      "change_hazard_prior_shape": 1,
      "days_until_scheduled_refresh": 5,
      "evidence_verified": true,
      "expected_queries_per_period": 1000,
      "id": "support",
      "refresh_interval_days": 30,
      "retrieval_failure_prior_alpha": 1,
      "retrieval_failure_prior_beta": 49,
      "source_age_days": 10,
      "stale_loss_prior_log_mean": 4.605170185988092,
      "stale_loss_prior_log_sd": 0.5,
      "value_per_grounded_answer": 5
    }
  ],
  "domain_history": [
    {
      "domain_id": "support",
      "evidence_verified": true,
      "id": "support-0",
      "period": 0,
      "query_count": 100,
      "source_change_count": 1,
      "source_exposure_days": 30,
      "stale_answer_count": 2,
      "stale_loss_log_squared_sum": 42.415184883827195,
      "stale_loss_log_sum": 9.210340371976184,
      "stale_loss_observation_count": 2,
      "unsupported_answer_count": 2
    },
    {
      "domain_id": "support",
      "evidence_verified": true,
      "id": "support-1",
      "period": 1,
      "query_count": 100,
      "source_change_count": 0,
      "source_exposure_days": 30,
      "stale_answer_count": 2,
      "stale_loss_log_squared_sum": 42.415184883827195,
      "stale_loss_log_sum": 9.210340371976184,

Truncated for display — the full payload is 101 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

{
  "assumptions": [
    "Complete zero-inclusive tenant history identifies source-change hazard and retrieval failure; priors are organization-owned, and sparse histories remain diagnostic rather than silently borrowing another tenant's rates.",
    "Source changes follow a Gamma-Poisson hazard, unsupported retrieval follows a Beta-Binomial model, and positive stale losses follow a partially pooled lognormal severity model.",
    "One scenario draw and one common index-failure state are shared across domains in each Monte Carlo path, preserving portfolio dependence instead of multiplying independent p50 estimates.",
    "The forecast supports refresh planning; it is not a guarantee of factual truth, a content-access authorization, legal/compliance advice, or a judgment about an author, team, vendor, or individual."
  ],
  "configuration": {
    "horizon_periods": 6,
    "maximum_stale_answer_fraction": 0.05,
    "minimum_history_periods": 4,
    "period_days": 30,
    "posterior_draws": 300,
    "seed": 11,
    "shared_common_index_failure_state": true,
    "shared_scenario_draws": true,
    "tail_probability": 0.05
  },
  "decision": "ai_knowledge_staleness_forecast_supported",
  "domain_forecasts": [
    {
      "domain_id": "support",
      "economic_loss_conditional_value_at_risk": 678035.4882,
      "economic_loss_value_at_risk": 314267.6674,
      "expected_economic_loss": 79164.0865,
      "expected_queries": 6225.0767,
      "expected_stale_answer_fraction": 0.0876,
      "expected_stale_answers": 563.0767,
      "expected_unsupported_answers": 136.3833,
      "history_period_count": 4,
      "posterior_mean_daily_change_hazard": 0.0044,
      "posterior_mean_retrieval_failure_probability": 0.0198,
      "probability_stale_fraction_breaches_limit": 0.4467
    }
  ],
  "failed_gates": [],
  "method": "tenant_bayesian_knowledge_change_refresh_loss_simulation_v1",
  "scenario_diagnostics": [
    {
      "configured_probability": 0.8,
      "expected_portfolio_economic_loss": 42391.7422,
      "realized_draw_count": 245,
      "scenario_id": "base"
    },

Truncated for display — the full payload is 67 lines.

How it works

Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.

1 Validate consecutive zero-inclusive domain-period history and fit Gamma source-change hazards, Beta retrieval-failure rates and partially pooled lognormal positive stale-loss severity from tenant data and governed priors.
2 Draw one common operating scenario and index-failure state per Monte Carlo path, then simulate daily source changes, scheduled refreshes, queries, unsupported retrieval and stale exposure for every domain.
3 Aggregate correlated paths into domain and portfolio stale fractions, breach probabilities, expected loss and economic-loss VaR/CVaR; abstain when local history or evidence is insufficient.

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

Training examples precede their outcomes, censoring and unresolved work are represented, and deployment populations remain comparable to validation cohorts.
Knowledge domains, refresh definitions, zero query/change periods, mature stale labels, positive loss marks and common scenarios share a stable prospective horizon.
A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
This conditional system forecast neither certifies factual truth nor attributes blame; do not transfer fitted rates or losses between tenants.

Minimum evidence

domain_history: required and organization-defined
current_domains: 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

stable knowledge-domain period spine joined to complete source change/version, scheduled refresh, query evaluation and finance loss histories without dropping zero periods, unresolved outcomes or common index incidents
domain/version epoch, stale/unsupported label and maturity, exposure/change/refresh semantics, loss perimeter, tenant priors, horizon/cadence, query/value plan, coherent scenario law, common index failure, evidence/support gates 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 stale and unsupported ai answers" }
  → finds "forecast_ai_knowledge_staleness_loss"

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

gitrevio_capability_run
  { "capability_id": "forecast_ai_knowledge_staleness_loss", "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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