Calculate customer concentration technology risk

Quantify the joint tail risk created when customer contribution is concentrated on shared technology, using coherent failure scenarios, non-additive dependency losses, CVaR, and overlapping component sensitivities.

Decision analysis Calculate CTOs Tech CEOs IT managers Team leads Investors

What it's for

Reveals when the biggest customers secretly depend on the same fragile technical choke point—and prices the joint downside rather than showing two disconnected concentration charts.

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
components array of objects (1 field) ≥ 1 item Evidence Yes
customers array of objects (3 fields) ≥ 2 items Evidence Yes
max_detail_rows integer ≥ 1, ≤ 500 Numerical control Optional
maximum_single_component_exposure_share number ≥ 0, ≤ 1 Your calibration Optional
maximum_single_customer_contribution_share number ≥ 0, ≤ 1 Your calibration Optional
maximum_tail_loss_fraction number ≥ 0, ≤ 1 Your calibration Optional
scenarios array of objects (3 fields) ≥ 2 items Evidence Yes
tail_probability number ≥ 0.001, ≤ 0.5 Your calibration Optional

Each customers record

Field Type Required
annual_contribution number (> 0) Yes
component_loss_fractions object Yes
id string (non-empty) Yes
Example input 
{
  "components": [
    {
      "id": "identity-service"
    },
    {
      "id": "primary-database"
    },
    {
      "id": "billing-provider"
    }
  ],
  "customers": [
    {
      "annual_contribution": 600000,
      "component_loss_fractions": {
        "identity-service": 0.7,
        "primary-database": 0.9
      },
      "id": "enterprise-a"
    },
    {
      "annual_contribution": 300000,
      "component_loss_fractions": {
        "billing-provider": 0.8,
        "identity-service": 0.5
      },
      "id": "enterprise-b"
    },
    {
      "annual_contribution": 250000,
      "component_loss_fractions": {
        "billing-provider": 0.3,
        "primary-database": 0.4
      },
      "id": "self-serve"
    }
  ],
  "maximum_single_component_exposure_share": 0.5,
  "maximum_single_customer_contribution_share": 0.4,
  "maximum_tail_loss_fraction": 0.25,
  "scenarios": [
    {
      "failed_component_ids": [],

Truncated for display — the full payload is 79 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": [
    "Annual contribution is finance-owned incremental contribution rather than revenue, customer identities and component dependencies are current, and exposure fractions estimate contribution loss conditional on component failure.",
    "Submitted scenarios are coherent joint component-failure states with probabilities on one horizon; multiple failures combine through multiplicative retained contribution to avoid adding the same customer loss above 100 percent.",
    "Component marginal loss removes one component from each represented scenario and is an overlapping sensitivity, not causal Shapley attribution; total-variation, omitted-event, recovery-time, and model uncertainty require separate stress tests.",
    "The result is company/customer portfolio risk—not evidence of employee fault, customer quality, nationality, misconduct, or an automatic decision to terminate a relationship."
  ],
  "component_diagnostics": [
    {
      "component_id": "identity-service",
      "exposed_contribution_share": 0.4957,
      "exposed_customer_count": 2,
      "loss_weighted_exposed_contribution": 570000,
      "marginal_expected_portfolio_loss_if_present": 43740,
      "marginal_failure_probability": 0.09
    },
    {
      "component_id": "primary-database",
      "exposed_contribution_share": 0.5565,
      "exposed_customer_count": 2,
      "loss_weighted_exposed_contribution": 640000,
      "marginal_expected_portfolio_loss_if_present": 43640,
      "marginal_failure_probability": 0.08
    },
    {
      "component_id": "billing-provider",
      "exposed_contribution_share": 0.2739,
      "exposed_customer_count": 2,
      "loss_weighted_exposed_contribution": 315000,
      "marginal_expected_portfolio_loss_if_present": 9450,
      "marginal_failure_probability": 0.03
    }
  ],
  "configuration": {
    "maximum_single_component_exposure_share": 0.5,
    "maximum_single_customer_contribution_share": 0.4,
    "maximum_tail_loss_fraction": 0.25,
    "multi_component_loss_rule": "multiplicative_survival_union",
    "scenario_count": 5,
    "tail_probability": 0.1
  },
  "customer_diagnostics": [
    {
      "annual_contribution": 600000,

Truncated for display — the full payload is 146 lines.

How it works

Decision analysis — Turn uncertainty, cost and risk appetite into a defensible choice, with the reasoning left inspectable.

  1. 1 Freeze finance-owned customer contribution, versioned customer-to-component conditional loss fractions, and coherent joint component-failure scenarios with probabilities on one horizon.
  2. 2 Combine simultaneous dependency failures through multiplicative retained contribution so the same customer cannot lose more than 100 percent, then calculate expected loss, VaR, exact weighted CVaR, customer HHI, and effective customer count.
  3. 3 Attribute the CVaR tail to customers, remove each component from represented scenarios for marginal sensitivity, and gate single-customer, shared-component, and total-tail concentrations separately.

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

  • Actions, outcomes, utilities, evidence boundaries, uncertainty representation, and accountable ownership match the actual decision.
  • Contribution is incremental rather than revenue, component dependencies and recovery horizon are current, loss fractions are conditional on failure, and scenarios represent joint states including common causes, recovery, and material omitted events.
  • The result structures a governed choice; it does not replace accountable judgment or authorize action outside the declared decision boundary.
  • Component marginals overlap and are not Shapley or causal blame; the output evaluates portfolio structure, never employee fault, customer quality, nationality, loyalty, or an automatic relationship decision.

Minimum evidence

  • components: at least 1 rows/items
  • customers: at least 2 rows/items
  • scenarios: at least 2 rows/items

How to validate it

Validate on future periods or held-out aggregate units, compare with a simple baseline, and require stability across plausible metric definitions and decision thresholds.

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

  • customer-component conditional contribution-loss fractions and coherent joint component-failure scenarios with calibrated probabilities on one horizon
  • customer and contribution perimeter, component boundary, dependency freshness, conditional loss semantics, scenario completeness, common causes, recovery horizon, probabilities, tail level, concentration limits, and validation epoch

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": "quantify the joint tail risk created" }
  → finds "calculate_customer_concentration_technology_risk"

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

gitrevio_capability_run
  { "capability_id": "calculate_customer_concentration_technology_risk", "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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