Forecast acquisition technology integration economics
Forecast acquisition-technology integration time, cost, stranded cost, synergy realization and economic-shortfall CVaR from pooled lognormal history, dependency paths, finite capacity and shared disruption states.
What it's for
Turns a technology acquisition plan into a board-ready distribution for completion, integration cost, stranded cost, realized synergy, negative-value probability and severe shortfall.
What you give it
Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.
| Field | Type | Role | Required |
|---|---|---|---|
| annual_discount_rate | number ≥ 0 | Your calibration | Optional |
| current_workstreams | array of objects (17 fields) | Evidence | Yes |
| historical_workstream_classes | array of objects (12 fields) | Evidence | Yes |
| horizon_months | number > 0 | Your calibration | Optional |
| integration_capacity_units | number > 0 | Your calibration | Yes |
| max_detail_rows | integer ≥ 1, ≤ 500 | Numerical control | Optional |
| minimum_completed_workstreams | 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 (9 fields) | Evidence | Yes |
| simulation_count | integer ≥ 1000, ≤ 1000000 | Your calibration | Optional |
| tail_probability | number > 0, < 1 | Your calibration | Optional |
Each current_workstreams
record
| Field | Type | Required |
|---|---|---|
| cost_prior_log_mean | number | Yes |
| cost_prior_log_sd | number (> 0, ≤ 5) | Yes |
| cost_prior_strength | number (> 0) | Yes |
| dependency_workstream_ids | array of string | Yes |
| duration_prior_log_mean_months | number | Yes |
| duration_prior_log_sd | number (> 0, ≤ 5) | Yes |
| duration_prior_strength | number (> 0) | Yes |
| evidence_verified | boolean | Yes |
| id | string (non-empty) | Yes |
| integration_group_id | string (non-empty) | Yes |
| monthly_stranded_cost | number (≥ 0) | Yes |
| monthly_synergy_value | number (≥ 0) | Yes |
| one_time_separation_cost | number (≥ 0) | Yes |
| required_capacity_units | number (> 0) | Yes |
| synergy_success_prior_alpha | number (> 0) | Yes |
| synergy_success_prior_beta | number (> 0) | Yes |
| workstream_class | string (non-empty) | Yes |
{
"current_workstreams": [
{
"cost_prior_log_mean": 11.512925464970229,
"cost_prior_log_sd": 0.3,
"cost_prior_strength": 5,
"dependency_workstream_ids": [],
"duration_prior_log_mean_months": 1.3862943611198906,
"duration_prior_log_sd": 0.3,
"duration_prior_strength": 5,
"evidence_verified": true,
"id": "application-migration",
"integration_group_id": "target-core",
"monthly_stranded_cost": 10000,
"monthly_synergy_value": 50000,
"one_time_separation_cost": 20000,
"required_capacity_units": 2,
"synergy_success_prior_alpha": 2,
"synergy_success_prior_beta": 2,
"workstream_class": "application"
},
{
"cost_prior_log_mean": 11.512925464970229,
"cost_prior_log_sd": 0.3,
"cost_prior_strength": 5,
"dependency_workstream_ids": [
"application-migration"
],
"duration_prior_log_mean_months": 1.3862943611198906,
"duration_prior_log_sd": 0.3,
"duration_prior_strength": 5,
"evidence_verified": true,
"id": "data-migration",
"integration_group_id": "target-core",
"monthly_stranded_cost": 10000,
"monthly_synergy_value": 40000,
"one_time_separation_cost": 20000,
"required_capacity_units": 2,
"synergy_success_prior_alpha": 2,
"synergy_success_prior_beta": 2,
"workstream_class": "data"
}
],
"historical_workstream_classes": [Truncated for display — the full payload is 409 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 historical workstream cohorts identify success; mature positive duration and cost observations identify pooled lognormal uncertainty; dependencies, capacity units, separation/stranded cost and synergy value share one integration perimeter.",
"The schedule combines a dependency critical path with a cumulative capacity-work lower bound; when capacity binds, workstream completion times scale proportionally rather than claiming an exact resource-constrained project schedule."
],
"counts": {
"current_workstreams": 2,
"history_rows": 24,
"integration_groups": 1,
"scenarios": 2,
"simulations": 2000,
"supported_workstreams": 2,
"workstream_classes": 2
},
"decision": "review_acquisition_integration_economics_and_capacity",
"forecast": {
"completion_p50_months": 8.4906,
"completion_p90_months": 13.2134,
"economic_shortfall_conditional_value_at_risk": 2684423.1015,
"economic_shortfall_value_at_risk": 2636788.8272,
"expected_completion_months": 9.8785,
"expected_gross_synergy_present_value": 2190204.9026,
"expected_integration_cost": 288031.4968,
"expected_net_integration_value": 1013468.861,
"expected_realized_synergy_present_value": 1443622.5987,
"expected_stranded_cost": 142122.2409,
"integration_cost_conditional_value_at_risk": 669362.7334,
"integration_cost_value_at_risk": 577499.9664,
"probability_completion_after_horizon": 0,
"probability_negative_net_integration_value": 0.137,
"tail_probability": 0.05
},
"limitations": [
"The forecast values a submitted technology integration case, not acquisition price, accounting purchase allocation, legal/tax outcome, employee retention, commercial synergy, authority to transact or a guaranteed completion date.",
"Unrepresented architecture discoveries, contractual restrictions, cultural change, security incidents, customer churn, nonlinear resource calendars and structural regime shifts can make the tail incomplete."
],
"method": "bayesian_lognormal_dag_capacity_acquisition_integration_economics_v1",
"reproducibility": {
"annual_discount_rate": 0.1,
"horizon_months": 36,
"random_seed": 91,
"scenario_ids": [
"base",
"stress"Truncated for display — the full payload is 75 lines.
How it works
Forecasting & survival — Estimate when something completes or fails, with censoring and unresolved work handled honestly rather than dropped.
- 1 Pool mature class-level log-duration and log-cost moments with governed workstream priors, and update synergy-success Beta priors from complete exposed/completed cohorts.
- 2 Draw coherent scenarios and one common disruption state per integration group, propagate work through the dependency DAG and combine its critical path with a cumulative capacity-work lower bound.
- 3 Discount realized synergy from simulated completion, retain separation and stranded cost, report time/cost/shortfall tails and abstain unless every current workstream has mature verified support.
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.
- Historical workstream cohorts include failures and unresolved exposure; completed positive duration/cost observations are mature; current dependencies, capacity, costs and technology synergy share one perimeter and horizon.
- A predictive interval or risk estimate is not a deadline promise, causal explanation, or individual-performance judgment.
- This is technology-integration economics—not acquisition price, commercial synergy, purchase accounting, legal/tax advice, employee-retention analysis or a guaranteed completion date.
Minimum evidence
- historical_workstream_classes: required and organization-defined
- current_workstreams: required and organization-defined
- scenarios: required and organization-defined
- integration_capacity_units: 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 workstream-class history pooled only within a governed comparability regime and joined to the current integration dependency graph, common platform groups, resource capacity, finance-reconciled costs and mature synergy realization without survivorship filtering
- workstream and completion boundary, cohort inclusion and zero policy, duration/cost currency and perimeter, synergy definition and maturity, dependency and capacity semantics, historical regime, forecast horizon, discount rate, common disruption scenarios, priors, 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 20 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 acquisitiontechnology integration time cost stranded" }
→ finds "forecast_acquisition_technology_integration_economics"
gitrevio_capability_describe
{ "capability_id": "forecast_acquisition_technology_integration_economics" }
→ returns the input schema and agent guidance shown on this page
gitrevio_capability_run
{ "capability_id": "forecast_acquisition_technology_integration_economics", "arguments": { ... } }
→ returns the result shown aboveWorks in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.