Optimize AI inference efficiency portfolio

What it's for

Turns AI cost cutting into a governed portfolio decision: the best feasible mix of cache, batch, retry and token changes without trading away quality or resilience invisibly.

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.

{
  "batch_capacity_requests_by_scenario": [
    500,
    500
  ],
  "cache_capacity_requests_by_scenario": [
    500,
    500
  ],
  "efficiency_options": [
    {
      "available_scenario_ids": [
        "base",
        "stress"
      ],
      "batch_fraction_scenarios": [
        0,
        0
      ],
      "cache_invalidation_tested": true,
      "cache_policy_compliant": true,
      "dependency_option_ids": [],
      "engineering_capacity_units": 0,
      "evidence_verified": true,
      "exclusion_option_ids": [],
      "id": "current",
      "implementation_cost": 0,
      "input_unit_reduction_fraction_scenarios": [
        0,
        0
      ],
      "is_current_state": true,
      "output_unit_reduction_fraction_scenarios": [
        0,
        0
      ],
      "p95_latency_ms_scenarios": [
        100,
        120
      ],
      "quality_score_scenarios": [
        0.9,
        0.88
      ],

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": [
    "Request volumes, retry fractions, units, prices, quality, latency and option effects share a scenario and billing perimeter; prospectively validated option evidence is required for every workload and scenario.",
    "Response caching is allowed only with explicit policy compliance and tested invalidation; quality and latency are hard local constraints that cannot be purchased with savings.",
    "Cache and batch loads are aggregated bottom-up across workloads before shared capacity is checked. Dependencies, exclusions, implementation cost and engineering capacity describe executable options.",
    "The selected portfolio is not deployment, procurement, privacy/security/legal approval, data-transfer authorization, a vendor SLA or a judgment about a provider, team or person."
  ],
  "baseline_current_state": {
    "expected_inference_cost": 17.8176,
    "expected_net_value": 804.5824,
    "option_ids": [
      "current"
    ]
  },
  "constraints": {
    "batch_capacity_requests_by_scenario": [
      500,
      500
    ],
    "cache_capacity_requests_by_scenario": [
      500,
      500
    ],
    "engineering_capacity_units": 5,
    "implementation_budget": 20,
    "maximum_cvar_economic_regret": null,
    "maximum_expected_economic_regret": null,
    "minimum_expected_portfolio_net_value": null,
    "tail_probability": 0.1
  },
  "decision": "ai_inference_efficiency_portfolio_supported",
  "efficiency_pareto_frontier": [
    {
      "economic_regret_cvar": 0,
      "engineering_capacity_units": 0,
      "expected_inference_cost": 17.8176,
      "expected_net_value": 804.5824,
      "implementation_cost": 0
    },
    {
      "economic_regret_cvar": 0,
      "engineering_capacity_units": 2,
      "expected_inference_cost": 11.0168,
      "expected_net_value": 810.3832,

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 Compute scenario-level inference cost, quality-adjusted value and shared cache/batch load for every workload option; reject local policy, invalidation, quality, latency or availability failures before economics.
2. 2 Enumerate the multiple-choice portfolio exactly when tractable, otherwise use a disclosed capacity-pruned beam while enforcing cross-workload dependencies/exclusions, implementation budget and engineering capacity.
3. 3 Compare every feasible state with the current-state baseline, optimize expected value minus CVaR regret appetite, and expose the economic Pareto frontier and solver certainty.

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": "choose one governed ai inference efficiency" }
  → finds "optimize_ai_inference_efficiency_portfolio"

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

gitrevio_capability_run
  { "capability_id": "optimize_ai_inference_efficiency_portfolio", "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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