Optimize contingent technology financing policy

What it's for

Moves beyond warning that runway is short: Gitrevio chooses what to commit now and what financing or roadmap response to trigger only if an observable funding state occurs.

What you give it

Every tool is a pure function: it computes from what you pass it and reads nothing on its own. Inputs split into evidence you supply from your connected systems, calibration your team owns, and numerical controls that affect precision but never the result's meaning.

{
  "actions": [
    {
      "budget_cost": 1,
      "capacity_units": 1,
      "covenant_ebitda_effect_scenarios": [
        0,
        0
      ],
      "debt_effect_scenarios": [
        0,
        0
      ],
      "decision_stage": "contingent",
      "dependency_ids": [],
      "effective_period": 1,
      "eligible_signal_ids": [
        "closed"
      ],
      "exclusion_ids": [],
      "gross_enterprise_value_effect_scenarios": [
        0,
        30
      ],
      "id": "equity-backstop",
      "transaction_cost_scenarios": [
        0,
        5
      ],
      "unrestricted_cash_effect_scenarios": [
        0,
        100
      ]
    }
  ],
  "contingent_budget_by_signal": {
    "closed": 1,
    "open": 0
  },
  "contingent_capacity_by_signal": {
    "closed": 1,
    "open": 0
  },
  "initial_budget": 0,

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": {
    "contingent_budget_by_signal": {
      "closed": 1,
      "open": 0
    },
    "contingent_capacity_by_signal": {
      "closed": 1,
      "open": 0
    },
    "initial_budget": 0,
    "initial_capacity_units": 0,
    "maximum_breach_probability": 0.1,
    "maximum_cvar_residual_funding_need": null,
    "maximum_net_leverage": 3,
    "minimum_cash_buffer": 20,
    "minimum_expected_net_enterprise_value": 0,
    "risk_aversion": 0,
    "tail_probability": 0.4
  },
  "decision": "contingent_technology_financing_policy_supported",
  "guardrails": [
    "Initial choices use no future signal. Every contingent choice is constant for all scenarios sharing the observed signal; effect and cost may vary afterward, but action selection cannot inspect hidden within-signal outcomes.",
    "Cash, debt, EBITDA, enterprise-value effects and transaction costs must be locally governed common-scenario estimates. Debt effects are not free cash unless unrestricted proceeds are also represented, and restructuring value loss must not be hidden as a financing benefit.",
    "Exact mode certifies only the represented scenario tree, actions and constraints. Beam mode is feasible but not globally optimal. No policy output is financing authority, covenant interpretation, solvency opinion, legal advice or permission to reduce safety-critical technology investment."
  ],
  "method": "nonanticipative_cash_debt_covenant_scenario_tree_policy_v1",
  "selected_contingent_actions": [
    {
      "action_id": "equity-backstop",
      "budget_cost": 1,
      "capacity_units": 1,
      "dependency_ids": [],
      "signal_id": "closed",
      "signal_probability": 0.4
    }
  ],
  "selected_initial_actions": [],
  "solver": {
    "beam_width": 100,
    "evaluated_policy_count": 2,
    "global_optimality_certificate": true,
    "maximum_exact_states": 4096,
    "mode": "exact_scenario_tree_enumeration",

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 a common finance scenario tree and define executable actions by decision stage, observable signal eligibility, effective period, cash/debt/EBITDA/value effects, transaction cost, resources and relations.
2. 2 Enumerate or beam-search initial sets and one contingent set per signal, applying the same choice to every hidden outcome inside that signal before rolling cash, debt and leverage paths forward.
3. 3 Select expected enterprise-value effect minus a CVaR funding-need penalty subject to breach and tail gates, returning the initial commitment, signal policy and an honest global-certificate flag.

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 20 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 initial and observedsignalcontingent financing restructuring" }
  → finds "optimize_contingent_technology_financing_policy"

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

gitrevio_capability_run
  { "capability_id": "optimize_contingent_technology_financing_policy", "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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