Optimize reserve follow on allocation

What it's for

Lets startup investors and tech CEOs price reserve strategy and contingent follow-on choices without granting the model future knowledge or hiding downside and opportunity cost inside a static company 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.

{
  "candidate_reserve_amounts": [
    5
  ],
  "companies": [
    {
      "id": "company-a"
    },
    {
      "id": "company-b"
    }
  ],
  "funding_options": [
    {
      "company_id": "company-a",
      "eligible_scenarios": [
        true,
        true
      ],
      "follow_on_amount": 5,
      "gross_terminal_value_scenarios": [
        12,
        1
      ],
      "id": "a-follow-on"
    },
    {
      "company_id": "company-b",
      "eligible_scenarios": [
        true,
        true
      ],
      "follow_on_amount": 5,
      "gross_terminal_value_scenarios": [
        8,
        6
      ],
      "id": "b-follow-on"
    }
  ],
  "maximum_cvar_loss": 0,
  "reserve_opportunity_cost_rate": 0,
  "scenarios": [
    {

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.

{
  "company_allocation_diagnostics": [
    {
      "company_id": "company-a",
      "expected_follow_on_spend": 2.5,
      "expected_gross_terminal_value": 6,
      "probability_funded": 0.5
    },
    {
      "company_id": "company-b",
      "expected_follow_on_spend": 2.5,
      "expected_gross_terminal_value": 3,
      "probability_funded": 0.5
    }
  ],
  "decision": "allocate_signal_contingent_follow_on_reserve",
  "guardrails": [
    "Every follow-on policy is constant inside the submitted signal partition; outcomes unavailable at the decision time cannot create a clairvoyant allocation.",
    "Gross terminal values, eligibility, dilution, ownership effects, opportunity cost, and scenario probabilities require point-in-time finance and investment-committee governance.",
    "Heuristic mode returns a deterministic supported policy and never claims global optimality or a complete Pareto frontier.",
    "Company allocation is a portfolio capital decision under submitted economics, not a founder, employee, nationality, integrity, or misconduct score and never authorizes an investment automatically."
  ],
  "method": "two_stage_signal_partition_follow_on_reserve_optimization_v1",
  "reserve_pareto_frontier": [
    {
      "clears_gates": true,
      "cvar_loss": 0,
      "expected_net_value": 0,
      "reserve_amount": 0,
      "risk_adjusted_value": 0,
      "value_of_available_signal": 0
    },
    {
      "clears_gates": true,
      "cvar_loss": -1,
      "expected_net_value": 4,
      "reserve_amount": 5,
      "risk_adjusted_value": 4,
      "value_of_available_signal": 2
    }
  ],
  "scenario_diagnostics": [
    {
      "follow_on_spend": 5,

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 companies, executable funding tiers, gross terminal values, eligibility, coherent scenarios, and the signal partition observable before each follow-on decision; scenarios inside one signal cell must share one policy.
2. 2 For every candidate reserve, solve a conditional multiple-choice capital allocation inside each signal cell, subtract follow-on spend and the ex-ante reserve opportunity cost, and propagate that non-clairvoyant policy back through all joint scenarios.
3. 3 Compare reserve sizes on expected net value, CVaR loss, risk-adjusted value, and a supported Pareto frontier; calculate the value of the submitted signal against one blind policy and disclose whether enumeration certifies the optimum or a deterministic heuristic only supports it.

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": "solve a twostage followon capital problem" }
  → finds "optimize_reserve_follow_on_allocation"

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

gitrevio_capability_run
  { "capability_id": "optimize_reserve_follow_on_allocation", "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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