{
  "name": "PurviewX Production Record",
  "description": "PurviewX's own engagements and internal ML models: what reached production, what we refused, and what we corrected. This is our own record, not an industry survey.",
  "methodology": "'In production' means processing real data in production use. 'Refused' means built, tested, and not shipped. 'Corrected' means we found and fixed a material error in our own output. The two de-listed security-assessment engagements are excluded.",
  "generated": "2026-07-20",
  "count": 10,
  "rows": [
    {
      "system": "Water-quality consumer product",
      "sector": "Energy",
      "outcome": "In production",
      "note": "Shipped on the fifth iteration, after four approaches failed. Now processes 50,000+ calls a month."
    },
    {
      "system": "Operations unification across five platforms",
      "sector": "Insurance",
      "outcome": "In production",
      "note": "One operational picture in 12 weeks; the engagement is ongoing."
    },
    {
      "system": "Property enrichment pipeline",
      "sector": "Distribution",
      "outcome": "In production",
      "note": "100,000+ properties enriched. We caught and reported 19% of our own spend as preventable waste."
    },
    {
      "system": "Court intelligence pipeline",
      "sector": "Legal",
      "outcome": "In production",
      "note": "75% cost reduction; first data in 8 weeks."
    },
    {
      "system": "Country-risk intelligence platform",
      "sector": "Enterprise security",
      "outcome": "In production",
      "note": "Country risk tied to enterprise exposure, built on official government advisory sources; ongoing."
    },
    {
      "system": "Daily customer-intelligence briefing suite",
      "sector": "Operations",
      "outcome": "In production",
      "note": "Automated daily briefings generated from unified operational data."
    },
    {
      "system": "Voice agent for live customer calls",
      "sector": "Operations",
      "outcome": "In production",
      "note": "Handles live inbound customer calls."
    },
    {
      "system": "Three predictive ML models",
      "sector": "Multiple",
      "outcome": "Refused",
      "note": "Built and tested. Accuracy collapsed once we removed leaked labels and circular targets, so we kept them shadow-only and shipped none of them."
    },
    {
      "system": "Edwin (AI memory system, our R&D)",
      "sector": "Own product",
      "outcome": "In production",
      "note": "Provisional patent filed. Two of 35 experiments disproved our own hypotheses, and the filing discloses them."
    },
    {
      "system": "Consumer marketplace directory",
      "sector": "Own product",
      "outcome": "Corrected",
      "note": "Found much of our own inventory had been inferred by a language model rather than observed; corrected the public count from 1,290 to 906 genuinely bookable."
    }
  ]
}