Optimize reliability investment frontier

What it's for

Lets CTOs see the actual efficient tradeoff between reliability spend, downtime, expected loss and catastrophic tail exposure instead of accepting one opaque priority score.

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": 60,
  "capacity_units": 5,
  "initiatives": [
    {
      "capacity_units": 1,
      "cost": 10,
      "dependency_ids": [],
      "downtime_reduction_hours_scenarios": [
        2,
        10,
        20
      ],
      "exclusion_ids": [],
      "id": "monitoring",
      "loss_reduction_scenarios": [
        20,
        100,
        200
      ]
    },
    {
      "capacity_units": 2,
      "cost": 30,
      "dependency_ids": [
        "monitoring"
      ],
      "downtime_reduction_hours_scenarios": [
        3,
        15,
        50
      ],
      "exclusion_ids": [],
      "id": "redundancy",
      "loss_reduction_scenarios": [
        30,
        150,
        500
      ]
    },
    {
      "capacity_units": 1,
      "cost": 15,
      "dependency_ids": [],

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": [
    "Every initiative and interaction uses the same coherent loss and downtime scenario columns, horizon, currency, availability definition and counterfactual; reductions are incremental, executable and jointly reconciled rather than independently claimed benefits added together.",
    "Dependencies and exclusions are complete, budget and capacity units are comparable, and scenario probabilities plus the tail level are governed. Capping reductions at baseline prevents negative residuals but does not repair omitted overlap or common-mode failure.",
    "Exact mode certifies the frontier only over every feasible submitted portfolio. Heuristic mode returns a supported set without a global certificate and must not be described as the complete Pareto frontier or a guaranteed optimum.",
    "The selected portfolio is the least-cost evaluated portfolio clearing declared aggregate downtime and CVaR targets; it is not an availability guarantee, causal effect, vendor score, staffing order or authorization to purchase, deploy or discontinue anything."
  ],
  "configuration": {
    "budget": 60,
    "capacity_units": 5,
    "maximum_cvar_loss": 350,
    "maximum_expected_downtime_hours": 12,
    "reductions_capped_at_scenario_baseline": true,
    "tail_probability": 0.1
  },
  "decision": "select_minimum_cost_target_clearing_reliability_portfolio",
  "method": "constrained_reliability_cost_downtime_tail_pareto_frontier_v1",
  "pareto_frontier": [
    {
      "capacity_units": 0,
      "cvar_residual_loss": 1000,
      "expected_residual_downtime_hours": 25,
      "expected_residual_loss": 250,
      "pareto_efficient": true,
      "passes_cvar_target": false,
      "passes_downtime_target": false,
      "selected_initiative_ids": [],
      "total_cost": 0
    },
    {
      "capacity_units": 1,
      "cvar_residual_loss": 800,
      "expected_residual_downtime_hours": 19.6,
      "expected_residual_loss": 196,
      "pareto_efficient": true,
      "passes_cvar_target": false,
      "passes_downtime_target": false,
      "selected_initiative_ids": [
        "monitoring"
      ],
      "total_cost": 10
    },
    {
      "capacity_units": 2,

How it works

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

1. 1 Freeze coherent baseline loss and downtime scenarios, candidate initiative cost/capacity, scenario reductions, dependencies, exclusions and all material pair synergies or overlap effects on one horizon and counterfactual.
2. 2 Enumerate every feasible portfolio inside the exact-state boundary or build a disclosed multiweight dependency-closure greedy supported set, cap reductions at each scenario baseline, and compute expected loss, CVaR loss and expected downtime.
3. 3 Remove four-objective dominated portfolios, preserve solver certainty, and choose the least-cost evaluated portfolio that clears the declared downtime and tail-loss targets—or retain the status quo or abstain when appropriate.

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": "construct a dependency and exclusionfeasible pareto" }
  → finds "optimize_reliability_investment_frontier"

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

gitrevio_capability_run
  { "capability_id": "optimize_reliability_investment_frontier", "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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