{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "@id": "https://www.insightits.com/products/prismshine.html",
  "slug": "prismshine",
  "canonicalName": "PrismShine",
  "name": "PrismShine — Anti-Hallucination Verdict Engine",
  "canonicalUrl": "https://www.insightits.com/products/prismshine.html",
  "knowledgeUrl": "https://www.insightits.com/catalog/prismshine.json",
  "markdownUrl": "https://www.insightits.com/catalog/prismshine.md",
  "family": "aiVerification",
  "infraFamily": "AI Verification",
  "recordType": "product",
  "status": "published",
  "parentProduct": null,
  "whatItIs": "Self-hosted anti-hallucination verdict engine: cause-side forensics (Tier-0 halt before tokens) plus effect-side grounding (Tiers 1–4) in one auditable ShineVerdict. PASS means grounded in the supplied preload — not world-true.",
  "problem": "Fluent answers are treated as truth. Teams need an enforceable pass / block / review gate plus evidence hashes, not only an offline faithfulness score.",
  "isNot": [
    "A firewall (that is PrismGuard)",
    "An agent runtime (that is ChorusGraph)",
    "A claim that PASS means world-true",
    "Streaming hallucination protection for already-streamed tokens",
    "A RAGAS performance row (not published)"
  ],
  "alternatives": [
    {
      "name": "HHEM-2.1-Open (Vectara)",
      "relationship": "Published vendor-authored HaluEval comparison on identical data and Azure ACI hardware. HHEM is a strong open factual-consistency classifier; PrismShine is an evidence-aware verdict engine with a runtime halt path.",
      "url": "https://www.insightits.com/compare/prismshine-vs-hhem.html"
    }
  ],
  "features": [
    {
      "name": "Cause + Effect Gate",
      "description": "Tier-0 halts empty retrieval, tool errors, and stale cache before tokens. Tiers 1–4 ground answers after generation."
    },
    {
      "name": "Auditable ShineVerdict",
      "description": "Named resolution_gate + evidence_hash on every decision — not a black-box score."
    },
    {
      "name": "Zero LLM on Default Path",
      "description": "Optional Tier-4 judge only on gray zone. pip install prismshine — no license key for OSS."
    }
  ],
  "architecture": "Tier 0 can halt empty retrieval, tool failure, stale cache, or incomplete trace before generation. Tiers 1–4 check answer support against the supplied preload. Optional ONNX Tier 3 (~1 GB, not in the bare wheel). ChorusGraph extra: pip install \"prismshine[chorusgraph]\" then require_shine / shine_node.",
  "useCases": [
    "Gate answers before they leave the agent",
    "Cause-side halt when retrieval or tools fail",
    "Auditable ShineVerdict with evidence hashes"
  ],
  "benchmarks": {
    "published": true,
    "summary": "Vendor-authored Azure ACI receipt 2026-07-20_run4_onnx: HaluEval QA n=200 F1 0.831 vs HHEM 0.746; numbers n=50 F1 1 vs 0.926. PASS means grounded in the supplied preload, not world-true.",
    "disclosures": [
      "The benchmark is vendor-authored.",
      "Bsum absolute F1 is modest for both systems.",
      "Tier-3 ONNX is an optional approximately 1 GB artifact and is not included in the bare wheel.",
      "The default path is English-centric.",
      "No RAGAS performance row has been completed.",
      "Do not present the evidence-aware runtime track as like-for-like content accuracy.",
      "No claim against RAGAS faithfulness.",
      "No claim against Guardrails AI provenance validators.",
      "No claim against NVIDIA NeMo Guardrails."
    ],
    "urls": [
      "https://github.com/insightitsGit/PrismShine/blob/main/docs/BENCHMARKS.md",
      "https://github.com/insightitsGit/PrismShine/tree/main/benchmarks/progress/2026-07-20_run4_onnx",
      "https://github.com/insightitsGit/PrismShine/blob/main/benchmarks/progress/2026-07-20_runtime_docker/FULL_REPORT.md"
    ],
    "tracks": [
      {
        "name": "HaluEval QA",
        "code": "B1",
        "n": 200,
        "prismShine": {
          "f1": 0.831,
          "precision": 0.916,
          "recall": 0.76,
          "auroc": 0.843,
          "p50Ms": 90,
          "llmCalls": 0
        },
        "hhem21Open": {
          "f1": 0.746,
          "precision": 0.857,
          "recall": 0.66,
          "auroc": 0.793,
          "p50Ms": 216,
          "llmCalls": 0
        }
      },
      {
        "name": "Fabricated and derived numbers",
        "code": "B2",
        "n": 50,
        "prismShine": {
          "f1": 1,
          "precision": 1,
          "recall": 1,
          "auroc": 1,
          "p50Ms": 20,
          "llmCalls": 0,
          "falsePositives": 0
        },
        "hhem21Open": {
          "f1": 0.926,
          "precision": 0.862,
          "recall": 1,
          "auroc": 1,
          "p50Ms": 166,
          "llmCalls": 0
        }
      },
      {
        "name": "HaluEval summarization",
        "code": "Bsum",
        "n": 50,
        "prismShine": {
          "f1": 0.6,
          "precision": 0.6,
          "recall": 0.6,
          "auroc": 0.562,
          "p50Ms": 1398,
          "llmCalls": 0
        },
        "hhem21Open": {
          "f1": 0.474,
          "precision": 0.692,
          "recall": 0.36,
          "auroc": 0.616,
          "p50Ms": 1899,
          "llmCalls": 0
        }
      }
    ]
  },
  "github": "https://github.com/insightitsGit/PrismShine",
  "pypi": "https://pypi.org/project/prismshine/0.2.2/",
  "documentation": [
    {
      "title": "Python hallucination detection guide",
      "url": "https://www.insightits.com/guides/hallucination-detection-python.html"
    },
    {
      "title": "PrismShine vs HHEM",
      "url": "https://www.insightits.com/compare/prismshine-vs-hhem.html"
    },
    {
      "title": "Hallucination detection tools",
      "url": "https://www.insightits.com/compare/hallucination-detection-tools.html"
    },
    {
      "title": "Benchmarks",
      "url": "https://github.com/insightitsGit/PrismShine/blob/main/docs/BENCHMARKS.md"
    },
    {
      "title": "Limits",
      "url": "https://github.com/insightitsGit/PrismShine/blob/main/docs/LIMITS.md"
    },
    {
      "title": "Integration",
      "url": "https://github.com/insightitsGit/PrismShine/blob/main/docs/INTEGRATION.md"
    },
    {
      "title": "https://github.com/insightitsGit/PrismShine/tree/main/benchmarks/progress/2026-07-20_run4_onnx",
      "url": "https://github.com/insightitsGit/PrismShine/tree/main/benchmarks/progress/2026-07-20_run4_onnx"
    },
    {
      "title": "readme",
      "url": "https://github.com/insightitsGit/PrismShine/blob/main/README.md"
    },
    {
      "title": "interactiveDemo",
      "url": "https://insightitsgit.github.io/PrismShine/demo.html"
    },
    {
      "title": "receiptRuntime",
      "url": "https://github.com/insightitsGit/PrismShine/tree/main/benchmarks/progress/2026-07-20_runtime_docker"
    },
    {
      "title": "fullReport",
      "url": "https://github.com/insightitsGit/PrismShine/blob/main/benchmarks/progress/2026-07-20_runtime_docker/FULL_REPORT.md"
    },
    {
      "title": "positioning",
      "url": "https://github.com/insightitsGit/PrismShine/blob/main/docs/POSITIONING.md"
    },
    {
      "title": "enterpriseWiringDemo",
      "url": "https://github.com/insightitsGit/PrismShine/blob/main/examples/enterprise_wiring_demo.py"
    }
  ],
  "install": "pip install prismshine (pin ==0.2.2 in docs). Apache-2.0 open core. Pro/Enterprise are pre-validation estimates.",
  "version": "0.2.2",
  "relatedProducts": [
    "chorusgraph",
    "prismguard",
    "prismrag"
  ],
  "publisher": {
    "@type": "Organization",
    "name": "Insight IT Solutions",
    "legalName": "Insight IT Solutions LLC",
    "url": "https://www.insightits.com"
  }
}