{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.21841",
  "title": "EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise",
  "summary": "EnterpriseVal replaces ad hoc prompt testing and public-benchmark-driven model selection with a formal use-case specification, a metric catalogue, calibrated LLM-as-judge grading via prediction-powered inference, and a threshold gate mapping metric confidence bounds to REJECT/CONDITIONAL/SCALE decisions. It costs engineering time to author specifications and gates, expert grading effort for judge calibration, and ongoing evaluation compute on each frozen configuration, none of which requires training infrastructure beyond existing API or single-GPU inference. It can fail through miscalibrated LLM judges, gates set without representative enterprise data, and pilot-validated catch rates that do not transfer to other workflows or consequence tiers.",
  "rationale": "The framework is implementable with API-level access and a small team, but its decision gates and value model have only single-organization pilot evidence, so the reader should validate it on one internal workflow before adopting it as standard practice.",
  "technique": "Use-case-level enterprise GenAI evaluation framework",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-09-18",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.21841",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "EnterpriseVal replaces ad hoc prompt testing and public-benchmark-driven model selection with a formal use-case specification, a metric catalogue, calibrated LLM-as-judge grading via prediction-powered inference, and a threshold gate mapping metric confidence bounds to REJECT/CONDITIONAL/SCALE decisions. It costs engineering time to author specifications and gates, expert grading effort for judge calibration, and ongoing evaluation compute on each frozen configuration, none of which requires training infrastructure beyond existing API or single-GPU inference. It can fail through miscalibrated LLM judges, gates set without representative enterprise data, and pilot-validated catch rates that do not transfer to other workflows or consequence tiers.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.21841",
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    {
      "claim_id": "claim-02",
      "statement": "In a banking pilot, analyst refinement effort for procedure transformation fell from an estimated 27.4 to 2.9 hours per document (about 9.4x), and credit-memo drafting reached 88% citation precision and 1.6% hallucination rate against gates of 70% and 5%.",
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      "source_url": "https://arxiv.org/abs/2609.21841",
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    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}