Optimize risk adjusted technology portfolio

What it's for

Answers a sharper version of ‘which projects are worth doing?’: which combination still creates value after charging scarce risk capital and counting common platform downside exactly once?

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.

{
  "budget": 35,
  "capital_charge_rate": 0.1,
  "confidence_level": 0.9,
  "cvar_loss_limit": 1000,
  "economic_capital_limit": 1000,
  "investments": [
    {
      "cost": 20,
      "dependency_ids": [],
      "direct_loss_by_scenario": {
        "adverse": 30,
        "ordinary": 5,
        "severe": 100
      },
      "evidence_verified": true,
      "exclusion_ids": [],
      "gross_value_by_scenario": {
        "adverse": 80,
        "ordinary": 100,
        "severe": 50
      },
      "id": "platform",
      "mandatory": false
    },
    {
      "cost": 10,
      "dependency_ids": [],
      "direct_loss_by_scenario": {
        "adverse": 21,
        "ordinary": 3.5,
        "severe": 70
      },
      "evidence_verified": true,
      "exclusion_ids": [],
      "gross_value_by_scenario": {
        "adverse": 56,
        "ordinary": 70,
        "severe": 35
      },
      "id": "data",
      "mandatory": false
    },
    {

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.

{
  "configuration": {
    "budget": 35,
    "capital_charge_rate": 0.1,
    "confidence_level": 0.9,
    "cvar_loss_limit": 1000,
    "economic_capital_limit": 1000,
    "eligible_investment_count": 3,
    "investment_count": 3,
    "minimum_expected_net_value": 0,
    "minimum_portfolio_raroc": 0.1,
    "scenario_count": 3
  },
  "decision": "portfolio_selected",
  "failed_gates": [],
  "finding": "risk_adjusted_technology_portfolio_identified",
  "governance": [
    "The optimizer chooses aggregate technology investments, not people, teams or employment actions.",
    "Scenario value, loss, currency, horizon and capital appetite remain finance/risk-owned inputs.",
    "A heuristic result is explicitly labeled incomplete and must be challenged against alternative portfolios before approval."
  ],
  "method": "economic_capital_raroc_pareto_exact_or_beam_v1",
  "pareto_frontier": [
    {
      "cost": 0,
      "economic_capital": 0,
      "expected_gross_value": 0,
      "expected_loss": 0,
      "expected_net_value": 0,
      "investment_ids": [],
      "loss_conditional_value_at_risk": 0,
      "loss_value_at_risk": 0,
      "raroc": null,
      "risk_adjusted_value": 0
    },
    {
      "cost": 5,
      "economic_capital": 31.2,
      "expected_gross_value": 35.6,
      "expected_loss": 8.8,
      "expected_net_value": 21.8,
      "investment_ids": [
        "product"
      ],

How it works

Constrained optimization — Pick the best feasible option under real limits — budget, headcount, dependencies, capacity — rather than ranking a list and hoping it fits.

1. 1 Admit only evidence-verified aggregate investments, close mandatory dependencies, preserve exclusions, align gross value/direct loss to common scenarios and activate each shared platform/provider loss only once for any selected member.
2. 2 For every exact or deterministic-beam candidate, calculate full cash cost, expected net value, loss VaR/CVaR, unexpected-loss economic capital, RAROC and value after the capital charge; reject candidates outside budget, capital, tail, value or RAROC appetite.
3. 3 Remove portfolios dominated jointly on cost, economic capital, CVaR and expected net value, then select the highest risk-adjusted feasible portfolio while disclosing search completeness, rejected evidence and the surviving Pareto frontier.

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.

Calibrating it to your org

Calibration workflow

1. 1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
2. 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. 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. 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. 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. 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": "choose a dependency and exclusionfeasible technology" }
  → finds "optimize_risk_adjusted_technology_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_risk_adjusted_technology_portfolio", "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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