{
  "accepted_char_count": 143,
  "assumption_density": 0.6428571428571429,
  "assumptions": [
    "Candidate estimate (inferred, not source-confirmed): 0.2% defect rate is accurate and represents true escapes, not under-detection by current manual process",
    "Candidate estimate (inferred, not source-confirmed): Defects span multiple classes rather than concentrating in 1-2 easily characterized types",
    "Candidate estimate (inferred, not source-confirmed): $400K budget is primarily one-time capex with limited tolerance for recurring operational costs",
    "Current manual QA line is the ground-truth authority (i.e., manual inspectors are catching what exists)",
    "Production environment has supplier lot changes and process variation that would challenge a static vision model"
  ],
  "axis_coverage": [
    {
      "axis_id": "correctness",
      "label": "Correctness",
      "status": "addressed",
      "finding": "Full replacement is incorrect given the statistical floor: 400 defects/month cannot validate multi-class detection at 99% catch/95% confidence without a 9-month minimum window. Per-class graduation is the only statistically defensible path.",
      "evidence": [
        "Rule-of-three: 3/n bound requires ~300 captured defects with zero misses",
        "At 400 defects/month spanning multiple classes, per-class samples may fall below 50"
      ],
      "gaps": [
        "Exact defect class count and per-class frequency unknown"
      ]
    },
    {
      "axis_id": "operability",
      "label": "Operability",
      "status": "addressed",
      "finding": "Shadow mode preserves current manual line operability while building validation data. Hybrid transition maintains throughput at 200K/month. Recurring $130K/yr engineering retainer is required beyond $400K capex.",
      "evidence": [
        "$180K capex + $130K/yr retainer + $90K reserve fits initial budget but creates ongoing cost",
        "Manual line retained as authority eliminates throughput risk during validation"
      ],
      "gaps": [
        "Team capacity for vision engineering not declared",
        "ROI payback period vs. manual labor cost not calculated"
      ]
    },
    {
      "axis_id": "failure_modes",
      "label": "Failure Modes",
      "status": "addressed",
      "finding": "Key failure modes: supplier lot change causing model drift, rare defect classes never reaching statistical validity, false-reject surge overwhelming exception queue, capex-only budgeting ignoring recurring engineering cost.",
      "evidence": [
        "Supplier lot change shifts reflectivity → false positive surge",
        "Classes \u003c20/month never reach 300-sample floor",
        "0.5% false-reject = ~1000 scrapped good units/month"
      ],
      "gaps": [
        "No queueing model for exception-handling capacity under surge conditions",
        "Degraded-mode behavior if vision system fails entirely not specified"
      ]
    }
  ],
  "censor_verdict": "BLOCKED",
  "confidence": 0.39,
  "confidence_breakdown": {
    "reasoning_quality": 0.6900000000000001,
    "evidence_strength": 0.3,
    "assumption_stability": 0.6
  },
  "coverage_report": {
    "axes": [
      {
        "axis": "correctness",
        "status": "addressed",
        "examples_from_verdict": [
          "Rule-of-three: 3/n bound requires ~300 captured defects with zero misses",
          "At 400 defects/month spanning multiple classes, per-class samples may fall below 50"
        ],
        "note": "sealed question axis addressed by structured verdict coverage"
      },
      {
        "axis": "operability",
        "status": "addressed",
        "examples_from_verdict": [
          "$180K capex + $130K/yr retainer + $90K reserve fits initial budget but creates ongoing cost",
          "Manual line retained as authority eliminates throughput risk during validation"
        ],
        "note": "sealed question axis addressed by structured verdict coverage"
      },
      {
        "axis": "failure_modes",
        "status": "addressed",
        "examples_from_verdict": [
          "Supplier lot change shifts reflectivity → false positive surge",
          "Classes \u003c20/month never reach 300-sample floor",
          "0.5% false-reject = ~1000 scrapped good units/month"
        ],
        "note": "sealed question axis addressed by structured verdict coverage"
      }
    ],
    "coverage_rate": 1,
    "summary": "Question-understanding coverage: 3 sealed axes/reframes checked."
  },
  "critical_unknown_cap": {
    "cap": 0.39,
    "summary": "Confidence capped at 39% because Manufacturing design is missing required grounding facts: operating_environment, actuator_or_process_constraints, degraded_mode, human_override, validation_evidence.",
    "triggers": [
      {
        "category": "operating_environment",
        "missing_fact": "What operating environment does this run in? Include facility, field conditions, jurisdiction, or deployment context."
      },
      {
        "category": "actuator_or_process_constraints",
        "missing_fact": "What actuator, equipment, capacity, material, route, or enforcement constraints bound the decision?"
      },
      {
        "category": "degraded_mode",
        "missing_fact": "What degraded, fallback, exception, or fail-safe mode exists when the system cannot continue normally?"
      },
      {
        "category": "human_override",
        "missing_fact": "Who can override or stop the system, and under what condition?"
      },
      {
        "category": "validation_evidence",
        "missing_fact": "What validation evidence exists? Include simulation, pilot data, field tests, acceptance tests, or historical outcomes."
      }
    ]
  },
  "domain_intent": {
    "primary_domain_id": "manufacturing_design",
    "primary_label": "Manufacturing design",
    "consequence_class": "physical_process",
    "source": "llm_enrichment",
    "confidence": 0.9,
    "rationale": "The question directly involves manufacturing process improvement with inspection line and defect rate.",
    "evidence": [
      "manual QA inspection",
      "machine vision",
      "defect rate"
    ],
    "candidates": [
      {
        "domain_id": "manufacturing_design",
        "label": "Manufacturing design",
        "consequence_class": "physical_process",
        "confidence": 0.9,
        "evidence": [
          "manual QA inspection",
          "machine vision",
          "defect rate"
        ]
      }
    ]
  },
  "evidence_boundary": {
    "observed_facts": [
      "Should we replace our manual QA inspection line with machine vision? 200K units/month, 3 shifts, 0.2% defect rate, $400K budget for automation."
    ],
    "assumptions": [
      "Candidate estimate (inferred, not source-confirmed): 0.2% defect rate is accurate and represents true escapes, not under-detection by current manual process",
      "Candidate estimate (inferred, not source-confirmed): Defects span multiple classes rather than concentrating in 1-2 easily characterized types",
      "Candidate estimate (inferred, not source-confirmed): $400K budget is primarily one-time capex with limited tolerance for recurring operational costs",
      "Current manual QA line is the ground-truth authority (i.e., manual inspectors are catching what exists)",
      "Production environment has supplier lot changes and process variation that would challenge a static vision model",
      "timeline synthetic default (not observed): no hard deadline assumed [synthetic] (not_addressed)",
      "reversibility synthetic default (not observed): assumed reversible within 90 days [synthetic] (not_addressed)",
      "team size synthetic default (not observed): standard team (5-10 engineers) [synthetic] (not_addressed)",
      "existing stack synthetic default (not observed): greenfield assumed [synthetic] (not_addressed)",
      "domain operating environment synthetic default (not observed): operating environment not specified [synthetic] (not_addressed)",
      "domain actuator or process constraints synthetic default (not observed): actuator/process constraints not specified [synthetic] (not_addressed)",
      "domain degraded mode synthetic default (not observed): degraded or fail-safe mode not specified [synthetic] (not_addressed)",
      "domain human override synthetic default (not observed): human override boundary not specified [synthetic] (not_addressed)",
      "domain validation evidence synthetic default (not observed): validation evidence not specified [synthetic] (not_addressed)"
    ],
    "inferred_specifics": [
      "Candidate estimate (inferred, not source-confirmed): Do NOT fully replace the manual QA line. Deploy machine vision in parallel shadow mode for 9 months, then graduate individual defect classes to vision-authority only when they individually clear 300+ captured instances with \u003c0.5% false-reject and zero escapes.. Because at 0.2% defect rate you generate only ~400 defects/month total, which is structurally insufficient to validate a vision system's catch rate across multiple defect classes — the rule-of-three requires ~300 captured defects with zero misses to claim 99% detection at 95% confidence, creating a minimum 9-month observation window before any class can be trusted to vision-only authority.. Key failure modes: Supplier lot change shifts surface reflectivity causing false-positive surge that overwhelms exception queue; Rare functional defect classes never accumulate statistical validity and create permanent hybrid complexity; Total captured defects \u003c250/month even at full volume means vision authority is unprovable on ANY class — project caps at advisory-only; Per-class sample starvation when defects span 8+ classes drops individual class counts below validation floor. Thresholds: ~400 defects/month at 200K units × 0.2%, 300 captured defects with zero misses per class for 99% catch at 95% confidence, 9-month minimum observation window, \u003c0.5% false-reject gate (=~1000 scrapped good units/month), $180K capex + $130K/yr vision-engineering retainer + $90K reserve, \u003c250 total monthly captured defects = hard kill condition",
      "Candidate estimate (inferred, not source-confirmed): Produce a defect taxonomy document listing every defect class with its monthly occurrence count over the last 12 months, categorized as dimensional/cosmetic/functional — this single artifact resolves the three-outcome decision tree and determines whether any class can reach statistical validation within budget.",
      "b004 had the highest confidence (0.93) among survivors and was consistently strengthened across rounds 2-4 by all three models. While prosecution noted it 'hides a NO inside a methodology narrative' and its defense quality was only 0.42, the branch's core statistical argument (400 defects/month is insufficient for multi-class validation) is mathematically sound and directly answers the binary question: full replacement NO, partial replacement conditionally YES after per-class validation. The killed b009 (0.86) was a strong salvage candidate with an excellent three-outcome decision tree — its specifics (per-class frequency as the single blocking fact, three-outcome structure) are incorporated into chosen_path. b002 (0.65) was substantively similar but less rigorous in its statistical grounding.",
      "Candidate estimate (inferred, not source-confirmed): Full replacement of manual QA with machine vision within $400K budget",
      "Candidate estimate (inferred, not source-confirmed): At 400 defects/month, there is no way to validate detection performance with statistical confidence before committing quality authority to the vision system. Full replacement creates undetectable quality escapes that surface only after supplier or process changes.",
      "Candidate estimate (inferred, not source-confirmed): Build parallel QA telemetry platform first, defer all vision deployment (b007)",
      "Candidate estimate (inferred, not source-confirmed): Adds unnecessary delay layer — telemetry collection can happen simultaneously with shadow-mode vision deployment rather than sequentially. Defense quality was lowest (0.58) and the branch offered no specific timeline, ROI benchmark, or decision gate.",
      "Candidate estimate (inferred, not source-confirmed): Deploy anomaly-detection-based vision (Cognex ViDi) instead of class-specific models (b006 salvage)"
    ],
    "inferred_specific_rows": [
      {
        "value": "at 0.2",
        "kind": "version",
        "where_introduced": "chosen_path",
        "basis": "synthetic"
      },
      {
        "value": "mode for 9 months",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "for"
      },
      {
        "value": "individually clear 300+ captured instances with \u003c0",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "with"
      },
      {
        "value": "instances with \u003c0.5% false-reject and zero escapes",
        "kind": "threshold",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "with"
      },
      {
        "value": "generate only ~400 defects/month total",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic"
      },
      {
        "value": "rule-of-three requires ~300 captured defects with zero",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "with"
      },
      {
        "value": "to claim 99% detection at 95% confidence",
        "kind": "threshold",
        "where_introduced": "chosen_path",
        "basis": "synthetic"
      },
      {
        "value": "detection at 95% confidence",
        "kind": "threshold",
        "where_introduced": "chosen_path",
        "basis": "synthetic"
      },
      {
        "value": "captured defects \u003c250/month even at full volume",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic"
      },
      {
        "value": "defects span 8+ classes drops individual class",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic"
      },
      {
        "value": "=~1000 scrapped good units/month",
        "kind": "config",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "units/month"
      },
      {
        "value": "$180K capex + $130K/yr vision-engineering",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "K"
      },
      {
        "value": "capex + $130K/yr vision-engineering retainer + $90K",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "K"
      },
      {
        "value": "retainer + $90K reserve",
        "kind": "estimate",
        "where_introduced": "chosen_path",
        "basis": "synthetic",
        "filing_anchor": "K"
      },
      {
        "value": "the last 12 months",
        "kind": "estimate",
        "where_introduced": "next_action",
        "basis": "synthetic"
      },
      {
        "value": "only 0.42",
        "kind": "version",
        "where_introduced": "selection_rationale",
        "basis": "synthetic"
      },
      {
        "value": "0.93",
        "kind": "estimate",
        "where_introduced": "selection_rationale",
        "basis": "synthetic"
      },
      {
        "value": "across rounds 2-4 by all three models",
        "kind": "estimate",
        "where_introduced": "selection_rationale",
        "basis": "synthetic"
      },
      {
        "value": "b004 had the highest confidence",
        "kind": "estimate",
        "where_introduced": "selection_rationale",
        "basis": "synthetic"
      },
      {
        "value": "400 defects/month is insufficient for multi-class validation",
        "kind": "estimate",
        "where_introduced": "selection_rationale",
        "basis": "synthetic",
        "filing_anchor": "for"
      }
    ],
    "unknowns": [
      "Number of distinct defect classes and their individual monthly frequencies",
      "Current manual inspection false-reject rate (to determine opportunity for improvement)",
      "Labor costs in USD per month for the 3-shift manual QA team",
      "Cheapest validation: Pilot a single-defect-class vision system on a Cognex In-Sight 2800 or Keyence CV-X platform for 1 SKU at full production speed and capture at least 300 defect instances over 8 weeks to verify detection and false-reject rates.",
      "Question assessment: This question is answerable as asked, but would benefit from refocusing to: 'Can machine vision be statistically validated to fully replace manual QA for any defect class under $400K automation budget?'",
      "Candidate estimate (inferred, not source-confirmed): Number of distinct defect classes and per-class monthly frequency — this single fact determines whether partial replacement is viable in 6-9 months or statistically impossible",
      "Whether defects are dimensional, cosmetic, or functional (functional defects may require test equipment beyond machine vision)",
      "Current manual inspection false-reject baseline — needed to benchmark vision system improvement",
      "Number of SKUs and changeover frequency — high-mix environments degrade vision system performance",
      "Candidate estimate (inferred, not source-confirmed): Actual labor cost of 3-shift manual QA line — needed for ROI calculation to justify $130K/yr recurring retainer",
      "Candidate estimate (inferred, not source-confirmed): Verdict is primarily model-reasoning; rule-of-three threshold (300 samples) is a standard statistical method but the specific application to machine vision validation gates is not externally grounded in the evidence provided",
      "Candidate estimate (inferred, not source-confirmed): Whether the $400K budget is one-time capex only or can accommodate recurring operational costs — if capex-only, the $130K/yr retainer creates a budget gap",
      "Team capacity for vision engineering and systems integration not declared in filing",
      "Candidate estimate (inferred, not source-confirmed): operational feasibility gap remains unresolved for Install and configure vision cell (Cognex In-Sight 2800 or Keyence CV-X) with lighting, fixturing, and integration to conveyor",
      "synthetic default is not observed evidence: timeline defaulted to no hard deadline assumed",
      "Candidate estimate (inferred, not source-confirmed): synthetic default is not observed evidence: reversibility defaulted to assumed reversible within 90 days",
      "Candidate estimate (inferred, not source-confirmed): synthetic default is not observed evidence: team_size defaulted to standard team (5-10 engineers)",
      "synthetic default is not observed evidence: existing_stack defaulted to greenfield assumed",
      "synthetic default is not observed evidence: domain_operating_environment defaulted to operating environment not specified",
      "synthetic default is not observed evidence: domain_actuator_or_process_constraints defaulted to actuator/process constraints not specified",
      "synthetic default is not observed evidence: domain_degraded_mode defaulted to degraded or fail-safe mode not specified",
      "synthetic default is not observed evidence: domain_human_override defaulted to human override boundary not specified",
      "synthetic default is not observed evidence: domain_validation_evidence defaulted to validation evidence not specified",
      "The Council could not reach a defensible verdict. Block reasons: confidence below threshold"
    ],
    "notice": "Concrete components, topology, and thresholds named below are candidate mitigations or example implementations inferred by the Council. They were not confirmed in your filing or established as part of your current environment."
  },
  "evidence_source_proof": {
    "schema_version": "oracul.evidence_source_proof.v1",
    "generated_at": "2026-06-27T03:21:22Z",
    "total_sources": 0,
    "contributed_sources": 0,
    "skipped_sources": 0,
    "summary": "Evidence sources: not shown on free tier - upgrade to see which sources contributed."
  },
  "grounding_note": "Concrete components, topology, and thresholds named below are candidate mitigations or example implementations inferred by the Council. They were not confirmed in your filing or established as part of your current environment.",
  "highest_probability_failure_mode": {
    "what_happens": "After shadow mode deployment, a supplier lot change shifts material surface properties (reflectivity, color, texture) causing the vision model's baseline reference to drift — units previously classified as normal now trigger anomaly flags while new lot's normal variation masks a genuine emerging defect class.",
    "why_it_is_hidden": "Shadow mode success metrics are computed against the CURRENT supplier lot and process state. A lot change is a discrete event that invalidates the confusion matrix built over months, but the system reports no degradation until the new lot's data flows through — by which time the manual line has already dispositioned the units and the vision system's silent disagreement is only visible in retrospective log analysis.",
    "detection_gap": "Standard monitoring tracks aggregate false-reject and escape rates averaged over days/weeks. A sudden lot-change-induced distribution shift may not trigger alerts if (a) the shift is gradual within a lot or (b) alert thresholds are set on weekly rolling averages rather than per-lot boundaries.",
    "blast_radius": "If the class has already graduated to vision-authority, escaped defects from the new lot ship before the per-lot confusion matrix is rebuilt. At 200K units/month, even 1-2 days of undetected escape at a new-lot defect rate can release 100+ defective units.",
    "monitoring_signal_to_add": "Track vision system confidence-score distribution per incoming material lot ID. Alert when median confidence drops \u003e1 standard deviation from the prior lot's baseline within the first 500 units of a new lot. Cross-reference with incoming material cert changes.",
    "severity": "high"
  },
  "id": "6d653f8d-36e4-4991-8bda-eb822c6bd094",
  "inferred_specifics": [
    {
      "value": "at 0.2",
      "kind": "version",
      "where_introduced": "chosen_path",
      "basis": "synthetic"
    },
    {
      "value": "mode for 9 months",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "for"
    },
    {
      "value": "individually clear 300+ captured instances with \u003c0",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "with"
    },
    {
      "value": "instances with \u003c0.5% false-reject and zero escapes",
      "kind": "threshold",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "with"
    },
    {
      "value": "generate only ~400 defects/month total",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic"
    },
    {
      "value": "rule-of-three requires ~300 captured defects with zero",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "with"
    },
    {
      "value": "to claim 99% detection at 95% confidence",
      "kind": "threshold",
      "where_introduced": "chosen_path",
      "basis": "synthetic"
    },
    {
      "value": "detection at 95% confidence",
      "kind": "threshold",
      "where_introduced": "chosen_path",
      "basis": "synthetic"
    },
    {
      "value": "captured defects \u003c250/month even at full volume",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic"
    },
    {
      "value": "defects span 8+ classes drops individual class",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic"
    },
    {
      "value": "=~1000 scrapped good units/month",
      "kind": "config",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "units/month"
    },
    {
      "value": "$180K capex + $130K/yr vision-engineering",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "K"
    },
    {
      "value": "capex + $130K/yr vision-engineering retainer + $90K",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "K"
    },
    {
      "value": "retainer + $90K reserve",
      "kind": "estimate",
      "where_introduced": "chosen_path",
      "basis": "synthetic",
      "filing_anchor": "K"
    },
    {
      "value": "the last 12 months",
      "kind": "estimate",
      "where_introduced": "next_action",
      "basis": "synthetic"
    },
    {
      "value": "only 0.42",
      "kind": "version",
      "where_introduced": "selection_rationale",
      "basis": "synthetic"
    },
    {
      "value": "0.93",
      "kind": "estimate",
      "where_introduced": "selection_rationale",
      "basis": "synthetic"
    },
    {
      "value": "across rounds 2-4 by all three models",
      "kind": "estimate",
      "where_introduced": "selection_rationale",
      "basis": "synthetic"
    },
    {
      "value": "b004 had the highest confidence",
      "kind": "estimate",
      "where_introduced": "selection_rationale",
      "basis": "synthetic"
    },
    {
      "value": "400 defects/month is insufficient for multi-class validation",
      "kind": "estimate",
      "where_introduced": "selection_rationale",
      "basis": "synthetic",
      "filing_anchor": "for"
    }
  ],
  "input_char_count": 143,
  "input_hard_char_limit": 24000,
  "input_limit_status": "ok",
  "input_soft_char_limit": 20000,
  "minority_report": {
    "content": "Reject the binary. Deploy a HYBRID staged inspection, not full replacement. Phase 1 (months 0-6, ~$150K): install machine vision (Cognex In-Sight 2800 or Keyence CV-X) ONLY on the dimensional/cosmetic defect classes that are high-volume, repeatable, and have clear visual signatures — let the system auto-pass the obvious-good and auto-fail the obvious-bad. Route the ~2-5% uncertain/borderline units to a single retained manual inspector per shift (cuts headcount but keeps human judgment on the long-tail). This is mandatory because at a 0.2% defect rate you only generate ~400 defects/month, which is structurally insufficient to train and validate a vision model on rare defect classes — full replacement creates a quality escape that surfaces only after tool wear or supplier lot change. Hard gate before any Phase 2 expansion: the vision system must demonstrate \u003c0.5% false-reject AND \u003e99% true-defect-catch rate measured on a HIGH-MIX day across a full supplier-lot change and a shift change, not a single-SKU demo. If false-reject exceeds 0.5% (=1000 scrapped good units/month), the recovery economics invert and you stop. Budget reserves $100K for a recurring vision-engineering retainer (~$130K/yr fully loaded) — capex-only budgeting is the hidden trap. BLOCKING MISSING FACTS that must be supplied before committing: (1) number of distinct SKUs and changeover frequency, (2) number of defect classes and per-class monthly frequency, (3) whether defects are dimensional, cosmetic, or functional, (4) current manual false-reject baseline to benchmark against.",
    "dissent_strength": 0.6499999999999999
  },
  "next_action": "Candidate estimate (inferred, not source-confirmed): Produce a defect taxonomy document listing every defect class with its monthly occurrence count over the last 12 months, categorized as dimensional/cosmetic/functional — this single artifact resolves the three-outcome decision tree and determines whether any class can reach statistical validation within budget.",
  "operational_feasibility": [
    {
      "required_action": "Install and configure vision cell (Cognex In-Sight 2800 or Keyence CV-X) with lighting, fixturing, and integration to conveyor",
      "required_capacity": {
        "skill": "Machine vision systems integrator",
        "hours": "8-12 weeks full-time engagement",
        "cadence": "one-shot"
      },
      "filed_capacity": "not declared",
      "gap": "No integrator or vision engineer mentioned in filing; $180K capex assumes vendor/integrator availability"
    },
    {
      "required_action": "Ongoing vision model tuning, retraining on lot changes, and confusion matrix maintenance during 9-month shadow period",
      "required_capacity": {
        "skill": "Vision engineering / ML ops",
        "hours": "~$130K/yr fully loaded (roughly 0.5-1 FTE equivalent)",
        "cadence": "ongoing"
      },
      "filed_capacity": "not declared",
      "gap": "$400K budget is stated as one-time; recurring $130K/yr retainer is not budgeted and represents a 33% annual cost beyond initial capex"
    },
    {
      "required_action": "Maintain full manual inspection line during shadow mode while also managing vision system exception routing",
      "required_capacity": {
        "skill": "QA inspectors (current headcount)",
        "hours": "3 shifts ongoing for 9+ months",
        "cadence": "ongoing"
      },
      "filed_capacity": "Current 3-shift manual QA line exists (implied by question)",
      "gap": "No gap for shadow mode — but labor savings are deferred 9+ months, so ROI timeline is longer than a capex-only analysis suggests"
    }
  ],
  "quality_gate": {
    "passed": false,
    "reasons": [
      "confidence below threshold"
    ]
  },
  "question": "Should we replace our manual QA inspection line with machine vision? 200K units/month, 3 shifts, 0.2% defect rate, $400K budget for automation.",
  "question_fit_score": 0.9,
  "rejected_alternatives": [
    {
      "path": "Candidate estimate (inferred, not source-confirmed): Full replacement of manual QA with machine vision within $400K budget",
      "rationale": "Candidate estimate (inferred, not source-confirmed): At 400 defects/month, there is no way to validate detection performance with statistical confidence before committing quality authority to the vision system. Full replacement creates undetectable quality escapes that surface only after supplier or process changes."
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): Build parallel QA telemetry platform first, defer all vision deployment (b007)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): Adds unnecessary delay layer — telemetry collection can happen simultaneously with shadow-mode vision deployment rather than sequentially. Defense quality was lowest (0.58) and the branch offered no specific timeline, ROI benchmark, or decision gate."
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): Deploy anomaly-detection-based vision (Cognex ViDi) instead of class-specific models (b006 salvage)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): Anomaly detection approach has theoretical merit for low-defect-rate environments but was not adequately defended against the concern that anomaly models still require statistical validation of their sensitivity, and the $150K+ ViDi license cost was not reconciled against the total budget with retainer needs."
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): Phased hybrid with 60-90 day pilot gate (b003 salvage)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): 60-90 day pilot is too short to accumulate sufficient defect samples for validation at 0.2% rate — would see only ~800 total defects, far too few for per-class confidence. The pilot would validate only on current conditions, missing the lot-change and drift scenarios that cause real escapes."
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b003 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 90 day (context: ...and low-frequency defect classes, and make full replacement contingent on a 60-90 day production pilot proving throughput at 200K units/month across 3 shifts. Preced...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b003 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 16 week (context: ...haracterized; typical installed cost is $100K-$250K for one robust cell, with 8-16 week deployment. This branch is more buildable than a full swap because it controls...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b005 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 90 day (context: ...shipped, because the rare 0.2% defect signal is statistically invisible in a 60-90 day pilot that may see fewer than ~800 genuine defects total, far too few to valida...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b005 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 0.2% (context: ...At 200K units/month, 0.2% defect rate means ~400 true defects/month against ~199,600 good units. The pilo...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b008 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 0.2% (context: ...tor shift change, or highest-mix day routes 5-10x normal exceptions (far beyond 0.2% baseline), starving WIP buffers downstream until rework cascades, violating ste...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b009 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 9 months (context: ...installed, 8-12 wk) reaches the 300-captured-defect / rule-of-three floor in 6-9 months per class; convert to vision-authority and cut to 1 retained inspector/shift. V...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b009 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 24 months (context: ...ulate the ~300 samples needed to validate 99% catch at 95% confidence even over 24 months, so vision authority on them is statistically unprovable and creates a quality...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b009 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 99% (context: ...ACE — those rare classes never accumulate the ~300 samples needed to validate 99% catch at 95% confidence even over 24 months, so vision authority on them is sta...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b009 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: 95% (context: ...rare classes never accumulate the ~300 samples needed to validate 99% catch at 95% confidence even over 24 months, so vision authority on them is statistically un...)"
    },
    {
      "path": "Candidate estimate (inferred, not source-confirmed): b009 (salvaged)",
      "rationale": "Candidate estimate (inferred, not source-confirmed): SALVAGED — specifics worth preserving: \u003e0.5% (context: ...able defect-catch authority on ANY class. Concrete kill threshold: false-reject \u003e0.5% (=1000 scrapped good units/month at 200K throughput) inverts recovery economics...)"
    }
  ],
  "reversal_conditions": [
    {
      "condition": "Candidate estimate (inferred, not source-confirmed): Defect taxonomy reveals ≤2 dominant defect classes each running \u003e200/month with clear visual signatures (dimensional or cosmetic only)",
      "flips_to": "Candidate estimate (inferred, not source-confirmed): Full replacement YES within $400K — single-class high-volume makes 300-sample validation achievable in 2-3 months, and vision authority can be established within a standard 90-day pilot"
    },
    {
      "condition": "Candidate estimate (inferred, not source-confirmed): Manual QA labor cost exceeds $500K/year and the $130K/yr vision retainer represents clear savings even during the shadow period",
      "flips_to": "Accelerate deployment with concurrent shadow + phased authority handover on fastest-validating classes, accepting slightly lower confidence thresholds to capture labor savings sooner"
    },
    {
      "condition": "Candidate estimate (inferred, not source-confirmed): Total captured defects \u003c250/month even at full volume OR defects are primarily functional (not visually detectable)",
      "flips_to": "Candidate estimate (inferred, not source-confirmed): Do NOT buy machine vision as a QA gate at all — redirect $400K to process controls, SPC, or functional test equipment instead"
    },
    {
      "condition": "Candidate estimate (inferred, not source-confirmed): 4-engineer team cannot dedicate ~1.0 FTE for 4 weeks to evidence gathering and pooler operation",
      "flips_to": "treat the pooler recommendation as provisional and staff operational readiness before rollout"
    }
  ],
  "structural_integrity": {
    "completeness": "complete",
    "reason_summary": "All required personas contributed to all required rounds."
  },
  "tier": "council",
  "unresolved_uncertainty": [
    "Candidate estimate (inferred, not source-confirmed): Number of distinct defect classes and per-class monthly frequency — this single fact determines whether partial replacement is viable in 6-9 months or statistically impossible",
    "Whether defects are dimensional, cosmetic, or functional (functional defects may require test equipment beyond machine vision)",
    "Current manual inspection false-reject baseline — needed to benchmark vision system improvement",
    "Number of SKUs and changeover frequency — high-mix environments degrade vision system performance",
    "Candidate estimate (inferred, not source-confirmed): Actual labor cost of 3-shift manual QA line — needed for ROI calculation to justify $130K/yr recurring retainer",
    "Candidate estimate (inferred, not source-confirmed): Verdict is primarily model-reasoning; rule-of-three threshold (300 samples) is a standard statistical method but the specific application to machine vision validation gates is not externally grounded in the evidence provided",
    "Candidate estimate (inferred, not source-confirmed): Whether the $400K budget is one-time capex only or can accommodate recurring operational costs — if capex-only, the $130K/yr retainer creates a budget gap",
    "Team capacity for vision engineering and systems integration not declared in filing",
    "Candidate estimate (inferred, not source-confirmed): operational feasibility gap remains unresolved for Install and configure vision cell (Cognex In-Sight 2800 or Keyence CV-X) with lighting, fixturing, and integration to conveyor",
    "synthetic default is not observed evidence: timeline defaulted to no hard deadline assumed",
    "Candidate estimate (inferred, not source-confirmed): synthetic default is not observed evidence: reversibility defaulted to assumed reversible within 90 days",
    "Candidate estimate (inferred, not source-confirmed): synthetic default is not observed evidence: team_size defaulted to standard team (5-10 engineers)",
    "synthetic default is not observed evidence: existing_stack defaulted to greenfield assumed",
    "synthetic default is not observed evidence: domain_operating_environment defaulted to operating environment not specified",
    "synthetic default is not observed evidence: domain_actuator_or_process_constraints defaulted to actuator/process constraints not specified",
    "synthetic default is not observed evidence: domain_degraded_mode defaulted to degraded or fail-safe mode not specified",
    "synthetic default is not observed evidence: domain_human_override defaulted to human override boundary not specified",
    "synthetic default is not observed evidence: domain_validation_evidence defaulted to validation evidence not specified",
    "The Council could not reach a defensible verdict. Block reasons: confidence below threshold"
  ],
  "url": "https://vectorcourt.com/v/6d653f8d-36e4-4991-8bda-eb822c6bd094",
  "verdict": "Candidate estimate (inferred, not source-confirmed): Do NOT fully replace the manual QA line. Deploy machine vision in parallel shadow mode for 9 months, then graduate individual defect classes to vision-authority only when they individually clear 300+ captured instances with \u003c0.5% false-reject and zero escapes.. Because at 0.2% defect rate you generate only ~400 defects/month total, which is structurally insufficient to validate a vision system's catch rate across multiple defect classes — the rule-of-three requires ~300 captured defects with zero misses to claim 99% detection at 95% confidence, creating a minimum 9-month observation window before any class can be trusted to vision-only authority.. Key failure modes: Supplier lot change shifts surface reflectivity causing false-positive surge that overwhelms exception queue; Rare functional defect classes never accumulate statistical validity and create permanent hybrid complexity; Total captured defects \u003c250/month even at full volume means vision authority is unprovable on ANY class — project caps at advisory-only; Per-class sample starvation when defects span 8+ classes drops individual class counts below validation floor. Thresholds: ~400 defects/month at 200K units × 0.2%, 300 captured defects with zero misses per class for 99% catch at 95% confidence, 9-month minimum observation window, \u003c0.5% false-reject gate (=~1000 scrapped good units/month), $180K capex + $130K/yr vision-engineering retainer + $90K reserve, \u003c250 total monthly captured defects = hard kill condition",
  "verdict_core": {
    "recommendation": "Candidate estimate (inferred, not source-confirmed): Do NOT fully replace the manual QA line. Deploy machine vision in parallel shadow mode for 9 months, then graduate individual defect classes to vision-authority only when they individually clear 300+ captured instances with \u003c0.5% false-reject and zero escapes.",
    "mechanism": "Candidate estimate (inferred, not source-confirmed): Because at 0.2% defect rate you generate only ~400 defects/month total, which is structurally insufficient to validate a vision system's catch rate across multiple defect classes — the rule-of-three requires ~300 captured defects with zero misses to claim 99% detection at 95% confidence, creating a minimum 9-month observation window before any class can be trusted to vision-only authority.",
    "tradeoffs": [
      "Candidate estimate (inferred, not source-confirmed): Manual labor costs continue for 9+ months with no immediate headcount reduction",
      "Candidate estimate (inferred, not source-confirmed): Rare defect classes (\u003c20/month) may never graduate and require permanent manual inspection",
      "Shadow mode doubles inspection overhead (vision + manual) during validation period"
    ],
    "failure_modes": [
      "Supplier lot change shifts surface reflectivity causing false-positive surge that overwhelms exception queue",
      "Rare functional defect classes never accumulate statistical validity and create permanent hybrid complexity",
      "Candidate estimate (inferred, not source-confirmed): Total captured defects \u003c250/month even at full volume means vision authority is unprovable on ANY class — project caps at advisory-only",
      "Candidate estimate (inferred, not source-confirmed): Per-class sample starvation when defects span 8+ classes drops individual class counts below validation floor"
    ],
    "thresholds": [
      "Candidate estimate (inferred, not source-confirmed): ~400 defects/month at 200K units × 0.2%",
      "Candidate estimate (inferred, not source-confirmed): 300 captured defects with zero misses per class for 99% catch at 95% confidence",
      "Candidate estimate (inferred, not source-confirmed): 9-month minimum observation window",
      "Candidate estimate (inferred, not source-confirmed): \u003c0.5% false-reject gate (=~1000 scrapped good units/month)",
      "Candidate estimate (inferred, not source-confirmed): $180K capex + $130K/yr vision-engineering retainer + $90K reserve",
      "Candidate estimate (inferred, not source-confirmed): \u003c250 total monthly captured defects = hard kill condition"
    ]
  },
  "verdict_posture": "analysis_only",
  "verdict_state_label": "deferred",
  "verdict_status": "insufficient",
  "verdict_type": "analysis"
}