Calculate financial value of modularity

Value modular architecture as a portfolio of exercisable future-change options, comparing architecture-specific cost, lead time, throughput capacity, discounting, value decay, downside CVaR, and the break-even modular investment.

Constrained optimization Calculate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Turns the vague promise that ‘modularity makes change cheaper’ into a board-ready financial value, downside distribution and explicit break-even architecture budget.

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.

Field	Type	Role	Required
change_options	array of objects (6 fields) ≥ 1 item	Evidence	Yes
discount_rate_per_period	number ≥ 0, ≤ 1	Your calibration	Optional
integrated_maximum_changes_exercised	integer ≥ 1, ≤ 1000	Your calibration	Optional
max_detail_rows	integer ≥ 1, ≤ 500	Numerical control	Optional
maximum_downside_cvar_loss	number ≥ 0	Your calibration	Optional
minimum_expected_modularity_value	number	Your calibration	Optional
modular_maximum_changes_exercised	integer ≥ 1, ≤ 1000	Your calibration	Optional
modular_present_value_overhead	number ≥ 0	Your calibration	Optional
modular_upfront_investment	number ≥ 0	Your calibration	Yes
risk_aversion	number ≥ 0	Your calibration	Optional
scenarios	array of objects (2 fields) ≥ 2 items	Evidence	Yes
tail_probability	number ≥ 0.001, ≤ 0.5	Your calibration	Optional
value_decay_rate_per_period	number ≥ 0, ≤ 10	Your calibration	Optional

Each `change_options` record

Field	Type	Required
benefit_scenarios	array of number (≥ 2 items)	Yes
id	string (non-empty)	Yes
integrated_implementation_cost_scenarios	array of number (≥ 2 items)	Yes
integrated_lead_time_periods_scenarios	array of number (≥ 2 items)	Yes
modular_implementation_cost_scenarios	array of number (≥ 2 items)	Yes
modular_lead_time_periods_scenarios	array of number (≥ 2 items)	Yes

Example input

{
  "change_options": [
    {
      "benefit_scenarios": [
        120,
        60
      ],
      "id": "new-market-rule",
      "integrated_implementation_cost_scenarios": [
        70,
        70
      ],
      "integrated_lead_time_periods_scenarios": [
        2,
        2
      ],
      "modular_implementation_cost_scenarios": [
        30,
        30
      ],
      "modular_lead_time_periods_scenarios": [
        0,
        0
      ]
    },
    {
      "benefit_scenarios": [
        100,
        40
      ],
      "id": "new-channel",
      "integrated_implementation_cost_scenarios": [
        70,
        50
      ],
      "integrated_lead_time_periods_scenarios": [
        3,
        2
      ],
      "modular_implementation_cost_scenarios": [
        25,
        25
      ],
      "modular_lead_time_periods_scenarios": [

Truncated for display — the full payload is 65 lines.

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.

Example output

{
  "assumptions": [
    "Integrated and modular alternatives face the same coherent future-change scenarios, benefit definitions, counterfactual, horizon, currency and probability law; implementation costs and lead times are architecture-specific and include testing, coordination, migration, compliance and operational consequences.",
    "Each architecture exercises only positive discounted net change options up to its governed scenario capacity, so modularity is valued as flexibility rather than assuming every forecast feature will be built. Shared resources, option interactions and path dependence absent from inputs remain omitted.",
    "Upfront investment and present-value overhead are complete and do not duplicate change costs. The break-even value is model-conditional, not an accounting intangible, resale valuation, causal estimate, architecture quality score or guarantee that future changes occur.",
    "The result supports an aggregate architecture investment review and does not authorize migration, procurement, outsourcing, staffing changes or evaluation of named engineers without technical, security, legal, finance and operating-owner approval."
  ],
  "change_option_diagnostics": [
    {
      "change_option_id": "new-channel",
      "expected_contribution_uplift": 45,
      "expected_integrated_policy_contribution": 0,
      "expected_modular_policy_contribution": 45,
      "integrated_exercise_probability": 0,
      "modular_exercise_probability": 1
    },
    {
      "change_option_id": "new-market-rule",
      "expected_contribution_uplift": 35,
      "expected_integrated_policy_contribution": 25,
      "expected_modular_policy_contribution": 60,
      "integrated_exercise_probability": 0.5,
      "modular_exercise_probability": 1
    }
  ],
  "configuration": {
    "discount_rate_per_period": 0,
    "exercise_rule": "select highest positive discounted net change options within architecture-specific scenario capacity",
    "integrated_maximum_changes_exercised": 1,
    "maximum_downside_cvar_loss": null,
    "minimum_expected_modularity_value": 0,
    "modular_maximum_changes_exercised": 2,
    "modular_present_value_overhead": 5,
    "modular_upfront_investment": 30,
    "risk_aversion": 0,
    "tail_probability": 0.5,
    "value_decay_rate_per_period": 0
  },
  "decision": "modularity_investment_clears_value_and_tail_gates",
  "method": "scenario_real_option_financial_value_of_modularity_v1",
  "scenario_diagnostics": [
    {
      "integrated_exercised_change_count": 0,
      "integrated_policy_value": 0,

Truncated for display — the full payload is 79 lines.

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 Freeze coherent future-change scenarios and estimate each option's benefit, implementation cost and lead time under the integrated and modular architectures on one economic basis.
2 For each scenario and architecture, discount and decay delivered benefit, exercise only the highest positive-net options inside its change-capacity constraint, then subtract modular upfront investment and present-value overhead.
3 Compare contingent policies through expected modularity value, positive-value probability, downside CVaR, risk-adjusted value and break-even upfront investment, and require both governed expected-value and tail gates to pass.

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.

Assumptions & guardrails

Objectives use commensurable locally governed value units, constraints reflect real feasibility, and uncertainty covers plausible adverse inputs.
The alternatives share one counterfactual and scenario law; architecture-specific cost and lead-time estimates include migration, testing, coordination, compliance and operations; omitted option interactions, shared resources and path dependence are immaterial or separately stressed.
The recommendation is optimal only for its stated objective, feasible set, evidence, and solver guarantee; it is not a universal management optimum.
The result is a model-conditional real-option comparison—not an accounting intangible, resale valuation, architecture quality score or migration authorization—and cannot justify procurement, outsourcing or staffing action by itself.

Minimum evidence

change_options: at least 1 rows/items
scenarios: at least 2 rows/items
modular_upfront_investment: required and organization-defined

How to validate it

Backtest the chosen action against simple feasible baselines on held-out scenarios, sweep costs/constraints/risk tolerance, and require constraint feasibility under adverse inputs.

Calibrating it to your org

Same for everyone

The mathematical kernel, validation rules, method version, and JSON output semantics are organization-independent; no tenant-trained coefficients or company benchmark is embedded in the function.

Specific to you

joint prospective change-demand scenarios and architecture counterfactual calibrated from point-in-time change histories, including migration, testing, coordination, compliance, reliability and operating consequences
architecture and option perimeter, scenario law, economic horizon and currency, discount and value-decay rates, exercise-capacity semantics, complete cost treatment, tail level, risk aversion, expected-value and downside gates, and accountable architecture/finance approval

Calibration workflow

1 Define the management decision, target outcome, aggregate unit, privacy boundary, cadence, and prediction/intervention horizon for this organization.
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 Estimate statistical parameters on training history, but obtain costs, utilities, risk tolerance, practical-effect thresholds, capacity, and policy constraints from accountable decision owners.
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 Deploy only if the returned decision clears evidence, overlap, calibration, robustness, and guardrail checks; warning, unsupported, schema-gap, and fallback decisions are abstentions.
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": "value modular architecture as a portfolio" }
  → finds "calculate_financial_value_of_modularity"

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

gitrevio_capability_run
  { "capability_id": "calculate_financial_value_of_modularity", "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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