Forecast executive technology commitment credibility
Recalibrate executive technology commitments with class-local isotonic Beta posteriors, then simulate correlated on-time outcomes, conditional lognormal delay, value erosion and financial-shortfall VaR/CVaR.
What it's for
Turns board commitments into a locally scored credibility distribution: what confidence historically meant here, how many commitments may land, and the expected and severe value shortfall.
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 |
|---|---|---|---|
| calibration_prior_strength | number > 0 | Your calibration | Optional |
| current_commitments | array of objects (14 fields) | Evidence | Yes |
| historical_commitment_bins | array of objects (14 fields) | Evidence | Yes |
| horizon_days | number > 0 | Your calibration | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_resolved_commitments | integer ≥ 1, ≤ 100000 | Your calibration | Optional |
| random_seed | integer ≥ 0, ≤ 4294967295 | Your calibration | Optional |
| scenarios | array of objects (7 fields) | Evidence | Yes |
| simulation_count | integer ≥ 1000, ≤ 200000 | Your calibration | Optional |
| tail_probability | number > 0, < 1 | Your calibration | Optional |
Each current_commitments
record
| Field | Type | Required |
|---|---|---|
| commitment_class | string (non-empty) | Yes |
| days_until_deadline | number (≥ 0) | Yes |
| delay_prior_log_mean_days | number | Yes |
| delay_prior_log_sd | number (> 0, ≤ 5) | Yes |
| delay_prior_strength | number (> 0) | Yes |
| evidence_verified | boolean | Yes |
| execution_group_id | string (non-empty) | Yes |
| id | string (non-empty) | Yes |
| reported_probability_on_time | number (≥ 0, ≤ 1) | Yes |
| shortfall_prior_log_mean | number | Yes |
| shortfall_prior_log_sd | number (> 0, ≤ 5) | Yes |
| shortfall_prior_strength | number (> 0) | Yes |
| value_if_on_time | number (≥ 0) | Yes |
| value_retention_fraction_if_late | number (≥ 0, ≤ 1) | Yes |
{
"current_commitments": [
{
"commitment_class": "delivery",
"days_until_deadline": 30,
"delay_prior_log_mean_days": 2.302585092994046,
"delay_prior_log_sd": 0.3,
"delay_prior_strength": 5,
"evidence_verified": true,
"execution_group_id": "core-platform",
"id": "platform-release",
"reported_probability_on_time": 0.8,
"shortfall_prior_log_mean": 11.512925464970229,
"shortfall_prior_log_sd": 0.3,
"shortfall_prior_strength": 5,
"value_if_on_time": 500000,
"value_retention_fraction_if_late": 0.8
}
],
"historical_commitment_bins": [
{
"achieved_on_time_count": 8,
"commitment_class": "delivery",
"delay_log_squared_sum": 106.03796220956798,
"delay_log_sum": 46.05170185988092,
"delay_observation_count": 20,
"eligible_commitment_count": 20,
"evidence_verified": true,
"id": "delivery-bin-0",
"probability_bin_lower": 0,
"probability_bin_upper": 0.5,
"resolved_commitment_count": 20,
"shortfall_log_squared_sum": 2650.949055239199,
"shortfall_log_sum": 230.25850929940458,
"shortfall_observation_count": 20
},
{
"achieved_on_time_count": 16,
"commitment_class": "delivery",
"delay_log_squared_sum": 106.03796220956798,
"delay_log_sum": 46.05170185988092,
"delay_observation_count": 20,
"eligible_commitment_count": 20,
"evidence_verified": true,Truncated for display — the full payload is 77 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 bins contain the complete eligible and mature resolved commitment population under stable definitions; isotonic calibration is local to each governed class and delay/shortfall moments include failed commitments rather than survivorship-filtered successes.",
"One coherent scenario and one common disruption state per execution group are shared across commitments; reported confidence is evidence to recalibrate, not a fact to preserve."
],
"calibration_diagnostics": [
{
"commitment_class": "delivery",
"eligible_commitments": 40,
"historical_on_time_fraction": 0.6,
"isotonic_brier_score": 0.2,
"isotonic_expected_calibration_error": 0,
"resolution_fraction": 1,
"resolved_commitments": 40,
"support_gate_pass": true
}
],
"commitment_forecasts": [
{
"commitment_class": "delivery",
"commitment_id": "platform-release",
"execution_group_id": "core-platform",
"expected_delay_if_late_days": 13.2399,
"expected_financial_shortfall": 58073.8194,
"probability_unrealized_by_horizon": 0,
"recalibrated_probability_on_time": 0.75,
"reported_probability_on_time": 0.8,
"support_gate_pass": true
}
],
"counts": {
"commitment_classes": 1,
"commitments": 1,
"execution_groups": 1,
"historical_bins": 2,
"scenarios": 2,
"simulations": 2000
},
"decision": "review_recalibrated_executive_commitment_portfolio",
"limitations": [
"The forecast does not prove executive intent, causality, accounting recognition, disclosure adequacy, a delivery guarantee or authority to revise a commitment.",
"Sparse classes, unrepresented regime changes, selective resolution, strategic interactions and losses outside the submitted perimeter can make the tail incomplete."
],
"method": "isotonic_beta_lognormal_common_regime_commitment_forecast_v1",Truncated for display — the full payload is 64 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 Build complete eligible and mature resolved commitment bins by stable class, repair empirical nonmonotonicity with weighted isotonic regression and retain Beta uncertainty around each fitted reliability bin.
- 2 Pool conditional late-delay and financial-shortfall log moments with governed priors, then draw one coherent scenario and common execution disruption per group.
- 3 Simulate joint on-time delivery, value retention, horizon miss and shortfall tails; abstain for sparse, unresolved, selectively observed or unverified classes.
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 bins include every eligible commitment and mature outcome under stable definitions; late delay and shortfall observations retain misses; current commitments map to comparable classes and execution groups.
- Posterior probability and adaptive selection are model-conditional; they are not substitutes for randomized propensities or guaranteed safety.
- A recalibrated probability is portfolio decision support—not evidence of executive intent, a delivery guarantee, accounting recognition, disclosure adequacy or authority to revise guidance.
Minimum evidence
- historical_commitment_bins: required and organization-defined
- current_commitments: required and organization-defined
- scenarios: required and organization-defined
- horizon_days: 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
- historical commitment eligibility spine joined to immutable original confidence, deadline, comparable class, mature on-time resolution, conditional delay and finance-reconciled shortfall without dropping unresolved, failed, late or zero cells
- commitment/class/version boundary, eligible and resolution policy, original confidence snapshot, on-time and maturity rule, delay and financial-shortfall perimeter, value retention, execution groups, forecast horizon, priors, scenario law, support 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": "recalibrate executive technology commitments with classlocal" }
→ finds "forecast_executive_technology_commitment_credibility"
gitrevio_capability_describe
{ "capability_id": "forecast_executive_technology_commitment_credibility" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_executive_technology_commitment_credibility", "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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