Optimize product mix

What it's for

Turns product strategy into an executable mix: which quantities to fund, what shared capacity they consume, and how much downside the plan carries.

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.

{
  "cash_budget": 500000,
  "mix_options": [
    {
      "cash_cost": 0,
      "contribution_scenarios": [
        0,
        0,
        0
      ],
      "id": "core-zero",
      "product_id": "core",
      "quantity": 0,
      "resource_requirements": {}
    },
    {
      "cash_cost": 300000,
      "contribution_scenarios": [
        200000,
        500000,
        800000
      ],
      "id": "core-growth",
      "product_id": "core",
      "quantity": 100,
      "resource_requirements": {
        "engineering": 5,
        "go_to_market": 3
      }
    },
    {
      "cash_cost": 0,
      "contribution_scenarios": [
        0,
        0,
        0
      ],
      "id": "enterprise-zero",
      "product_id": "enterprise",
      "quantity": 0,
      "resource_requirements": {}
    },
    {
      "cash_cost": 400000,

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": [
    "Each product has a true do-nothing option; exactly one submitted option is chosen per product, and quantity, cash, resource use, and contribution share one planning horizon.",
    "Scenario contribution is incremental after variable operating effects but its treatment of submitted cash cost must be finance-governed consistently; shared scenario columns preserve cross-product dependence.",
    "Only submitted shared resources constrain the mix; demand substitution, minimum lots, ramp timing, tax, working capital, and product interactions require explicit options or a richer model.",
    "Exact mode certifies only the represented discrete search; coordinate heuristic mode has no global optimality certificate and should be benchmarked on smaller exact instances before consequential use."
  ],
  "configuration": {
    "cash_budget": 500000,
    "maximum_exact_states": 262144,
    "option_count": 4,
    "product_count": 2,
    "represented_state_count": 4,
    "resource_count": 2,
    "risk_aversion": 0.5,
    "scenario_count": 3,
    "solver_mode": "exact_cartesian_enumeration",
    "state_count_is_capped": false,
    "tail_probability": 0.1
  },
  "decision": "product_mix_optimized_exact",
  "method": "discrete_shared_capacity_product_mix_cvar_v1",
  "product_diagnostics": [
    {
      "cash_cost": 400000,
      "expected_contribution": 730000,
      "is_safe_zero_option": false,
      "product_id": "enterprise",
      "selected_option_id": "enterprise-launch",
      "selected_quantity": 20
    },
    {
      "cash_cost": 0,
      "expected_contribution": 0,
      "is_safe_zero_option": true,
      "product_id": "core",
      "selected_option_id": "core-zero",
      "selected_quantity": 0
    }
  ],
  "resource_diagnostics": [
    {
      "capacity": 10,
      "resource_id": "engineering",

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 products, a true zero-production option for each, feasible discrete quantity alternatives, cash cost, shared resource consumption, and aligned incremental contribution scenarios.
2. 2 Enumerate the Cartesian product within the governed state limit and maximize expected contribution minus the CVaR-loss penalty subject to cash and every shared capacity; otherwise run visible coordinate-ascent screening.
3. 3 Return product quantities, cash slack, resource utilization, expected contribution, loss tail, and negative-contribution probability without promoting the fallback to an optimum.

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 one discrete quantity option per" }
  → finds "optimize_product_mix"

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

gitrevio_capability_run
  { "capability_id": "optimize_product_mix", "arguments": { ... } }
  → returns the result shown above

Works in Claude Desktop, Claude Code, Cursor, Cline, Continue.dev, Goose and Aider. See the MCP server.

Related tools

See every tool in Investment & portfolio choice →