Forecast AI output IP claim liability
Forecast aggregate AI-output IP claim frequency, gross cost, defense and disruption, collectible indemnity and net VaR/CVaR using tenant-local Bayesian recurrence/severity evidence plus shared provider events and counterparty default.
What it's for
Turns AI-output IP uncertainty into board-ready gross exposure, collectible risk transfer, reserve need and severe-loss 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 |
|---|---|---|---|
| claim_history | array of objects (11 fields) | Evidence | Yes |
| coefficient_grid_size | integer ≥ 11, ≤ 41 | Your calibration | Optional |
| coefficient_prior_sd | number > 0, ≤ 20 | Your calibration | Optional |
| current_exposures | array of objects (19 fields) | Evidence | Yes |
| horizon_years | number > 0 | Your calibration | Optional |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| maximum_absolute_coefficient | number > 0, ≤ 10 | Your calibration | Optional |
| minimum_claims | integer ≥ 1, ≤ 1000000 | Your calibration | Optional |
| minimum_history_rows | integer ≥ 1, ≤ 1000000 | Your calibration | Optional |
| random_seed | integer ≥ 0, ≤ 4294967295 | Your calibration | Optional |
| scenarios | array of objects (11 fields) | Evidence | Yes |
| simulation_count | integer ≥ 1000, ≤ 1000000 | Your calibration | Optional |
| tail_probability | number > 0, < 1 | Your calibration | Optional |
Each current_exposures
record
| Field | Type | Required |
|---|---|---|
| active_artifact_count | integer (≥ 1, ≤ 1000000) | Yes |
| claim_rate_prior_artifact_years | number (> 0) | Yes |
| claim_rate_prior_shape | number (> 0) | Yes |
| counterparty_group_id | string (non-empty) | Yes |
| defense_cost_per_claim | number (≥ 0) | Yes |
| evidence_verified | boolean | Yes |
| forecast_commercial_fraction | number (≥ 0, ≤ 1) | Yes |
| forecast_jurisdiction_risk_score | number (≥ 0, ≤ 1) | Yes |
| forecast_similarity_flag_fraction | number (≥ 0, ≤ 1) | Yes |
| id | string (non-empty) | Yes |
| indemnity_coverage_fraction | number (≥ 0, ≤ 1) | Yes |
| indemnity_deductible_per_claim | number (≥ 0) | Yes |
| indemnity_limit_per_claim | number (≥ 0) | Yes |
| operational_disruption_per_claim | number (≥ 0) | Yes |
| output_class | string (non-empty) | Yes |
| provider_group_id | string (non-empty) | Yes |
| severity_prior_log_mean | number | Yes |
| severity_prior_log_sd | number (> 0, ≤ 5) | Yes |
| severity_prior_strength | number (> 0) | Yes |
{
"claim_history": [
{
"claim_count": 0,
"commercial_fraction": 0.5,
"evidence_verified": true,
"exposure_artifact_years": 1000,
"gross_claim_cost_log_squared_sum": 0,
"gross_claim_cost_log_sum": 0,
"gross_claim_cost_observation_count": 0,
"id": "code-output-history-00",
"jurisdiction_risk_score": 0.5,
"output_class": "code-output",
"similarity_flag_fraction": 0.1
},
{
"claim_count": 2,
"commercial_fraction": 0.5,
"evidence_verified": true,
"exposure_artifact_years": 1000,
"gross_claim_cost_log_squared_sum": 196.15813140647603,
"gross_claim_cost_log_sum": 19.806975105072254,
"gross_claim_cost_observation_count": 2,
"id": "code-output-history-01",
"jurisdiction_risk_score": 0.5,
"output_class": "code-output",
"similarity_flag_fraction": 0.9
},
{
"claim_count": 0,
"commercial_fraction": 0.5,
"evidence_verified": true,
"exposure_artifact_years": 1000,
"gross_claim_cost_log_squared_sum": 0,
"gross_claim_cost_log_sum": 0,
"gross_claim_cost_observation_count": 0,
"id": "code-output-history-02",
"jurisdiction_risk_score": 0.5,
"output_class": "code-output",
"similarity_flag_fraction": 0.1
},
{
"claim_count": 2,
"commercial_fraction": 0.5,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.
{
"assumptions": [
"Complete zero-inclusive artifact-year exposure identifies claim frequency; mature positive gross-claim costs identify severity; similarity, commercialization and jurisdiction scores are point-in-time governed covariates rather than legal findings.",
"Common provider claim events and counterparty defaults are shared within declared groups, while limits, deductibles and coverage apply to submitted financial scenarios.",
"All fitted parameters remain tenant-local and predictive unless claim history comes from a design that identifies a causal control effect."
],
"counts": {
"counterparty_groups": 1,
"history_rows": 24,
"output_classes": 1,
"provider_groups": 1,
"scenarios": 2,
"simulations": 2000,
"supported_output_classes": 1
},
"decision": "review_ai_output_ip_claim_tail_and_risk_transfer",
"exposure_forecasts": [
{
"counterparty_group_id": "provider-a-indemnity",
"expected_claims": 0.4,
"expected_gross_claim_cost": 13785.9679,
"expected_indemnity_recovery": 9470.3876,
"expected_net_liability": 7660.5803,
"exposure_id": "code-output-current",
"historical_claims": 24,
"history_rows": 24,
"output_class": "code-output",
"provider_group_id": "provider-a",
"support_gate_pass": true
}
],
"forecast": {
"expected_claims": 0.4,
"expected_gross_claim_cost": 13785.9679,
"expected_indemnity_recovery": 9470.3876,
"expected_net_liability": 7660.5803,
"median_net_liability": 0,
"net_liability_conditional_value_at_risk": 122353.4413,
"net_liability_value_at_risk": 23972.9139,
"tail_probability": 0.05
},
"limitations": [
"This is an internal financial planning model, not legal advice, an infringement finding, a damages estimate, a coverage opinion or a prediction about any claimant or contributor.",
"Sparse claims, changing law, injunctions, unregistered artifacts, omitted corpora, aggregate limits, defense allocation and contested indemnity can make represented tails materially incomplete."Truncated for display — the full payload is 63 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 Fit a shared Bayesian similarity/commercialization/jurisdiction claim-hazard surface while retaining governed output-class Gamma-Poisson baselines from complete zero-inclusive artifact-year exposure.
- 2 Pool mature gross-claim log-cost moments with class priors, then simulate coherent future regimes, additive common-provider claim bursts and shared indemnity-counterparty default.
- 3 Apply deductibles, per-claim limits, coverage fractions, defense and disruption to report gross cost, collectible recovery, net liability, VaR/CVaR and explicit support abstention.
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.
- Claim history is complete and mature at aggregate class grain, similarity/jurisdiction scores are governed covariates rather than legal findings, and provider/counterparty grouping preserves material dependence.
- Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.
- This is internal financial planning, not an infringement finding, claimant forecast, damages estimate, legal advice or coverage opinion; predictive coefficients are not causal without a valid design.
Minimum evidence
- claim_history: required and organization-defined
- current_exposures: 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
- versioned aggregate claim panel joining only pre-claim output exposure and mature finance/legal outcomes while preserving provider and recovery-counterparty dependence
- claim/exposure/maturity definitions, similarity and jurisdiction score meaning, output classes, prior transport, support floors, provider/counterparty groups, enforceable recovery, finance loss, scenarios, horizon, tail appetite and model 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": "forecast aggregate aioutput ip claim frequency" }
→ finds "forecast_ai_output_ip_claim_liability"
gitrevio_capability_describe
{ "capability_id": "forecast_ai_output_ip_claim_liability" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_ai_output_ip_claim_liability", "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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