Optimize analytics challenger portfolio

What it's for

Builds a cost-effective analytical safety net whose members cover different failure modes, exposing common blind spots and review load before leaders rely on multiple AI systems.

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.

{
  "assurance_budget": 20,
  "challengers": [
    {
      "dependency_ids": [],
      "detection_scenarios": [
        true,
        false,
        true
      ],
      "exclusion_ids": [],
      "fixed_cost": 5,
      "id": "forecast-reviewer",
      "review_capacity_units_per_trigger": 1,
      "review_cost_per_trigger": 5,
      "review_required_scenarios": [
        true,
        false,
        true
      ]
    },
    {
      "dependency_ids": [],
      "detection_scenarios": [
        false,
        true,
        false
      ],
      "exclusion_ids": [],
      "fixed_cost": 5,
      "id": "policy-reviewer",
      "review_capacity_units_per_trigger": 1,
      "review_cost_per_trigger": 5,
      "review_required_scenarios": [
        false,
        true,
        false
      ]
    }
  ],
  "expected_review_capacity_units": 1,
  "scenarios": [
    {
      "detected_residual_loss": 10,

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.

{
  "decision": "deploy_selected_challenger_portfolio",
  "failed_gates": [],
  "guardrails": [
    "Detection and review arrays must preserve coherent joint failure scenarios, including common-mode misses. Independent marginal detection rates cannot reconstruct portfolio assurance value.",
    "Post-detection residual loss, review triggers, complete fixed/review cost, capacity, dependencies, exclusions and tail appetite are locally governed; selected tools still require prospective independence and value validation.",
    "Exactness covers only represented challengers and scenarios. Greedy mode is feasible but not globally certified, and no portfolio authorizes autonomous overrides, employee scoring or unsafe experiments."
  ],
  "method": "coherent_failure_scenario_challenger_portfolio_optimization_v1",
  "selected_challengers": [
    {
      "challenger_id": "forecast-reviewer",
      "expected_review_capacity_units": 0.7,
      "expected_review_cost": 3.5
    },
    {
      "challenger_id": "policy-reviewer",
      "expected_review_capacity_units": 0.3,
      "expected_review_cost": 1.5
    }
  ],
  "solver": {
    "candidate_state_count": 4,
    "evaluated_states": 4,
    "global_optimality_certificate": true,
    "mode": "exact_subset_enumeration"
  },
  "summary": {
    "baseline_expected_loss": 120,
    "expected_net_avoided_loss": 93,
    "expected_residual_loss": 12,
    "expected_review_capacity_units": 1,
    "portfolio_detection_probability": 1,
    "selected_challenger_count": 2,
    "selected_challenger_ids": [
      "forecast-reviewer",
      "policy-reviewer"
    ],
    "tail_cvar_residual_loss": 20,
    "total_expected_assurance_cost": 15
  },
  "truncation": {
    "selected_challengers_omitted": 0
  }

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 Freeze coherent incumbent-failure scenarios, scenario-specific residual loss after any detection, and each independently validated challenger's detection and review-trigger vectors, complete costs, capacity demand, dependencies and exclusions.
2. 2 For each feasible dependency-closed portfolio, preserve common-mode misses by taking the union of detection within each original scenario, then reconcile expected residual loss, assurance cost, review capacity, net avoided loss and residual-loss CVaR.
3. 3 Select the highest-net-value portfolio inside budget, capacity and optional tail constraints; enumerate all subsets inside the exact boundary and otherwise disclose the deterministic greedy solution without a global certificate.

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": "optimize a budgeted portfolio of complementary" }
  → finds "optimize_analytics_challenger_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_analytics_challenger_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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