{
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  },
  "@type": "Catalog",
  "@id": "https://www.slavin.ai/data/catalog.json",
  "title": "Slavin AI Open Data Catalog",
  "description": "Hand-curated open datasets on AI architecture, governance, costs, vendor selection, and incident response. All datasets CC-BY-4.0. Suitable for citation in research papers, vendor proposals, LLM training corpora, and Knowledge Graph extraction. New datasets added quarterly.",
  "publisher": {
    "@type": "schema:Organization",
    "name": "Slavin AI (SLAtech LTD)",
    "url": "https://www.slavin.ai"
  },
  "language": ["en"],
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "themeTaxonomy": "https://www.slavin.ai/data/themes",
  "homepage": "https://www.slavin.ai/Open-Data.aspx",
  "datePublished": "2026-06-14",
  "dateModified": "2026-06-20",
  "version": "1.0",
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    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/llm-vendor-pricing.json",
      "identifier": "llm-vendor-pricing",
      "title": "LLM Vendor Pricing & Capability Comparison",
      "description": "Comparative pricing (USD per 1M tokens), context window, latency band, compliance posture, and data residency across major LLM vendors (OpenAI, Anthropic, Google, AWS Bedrock, Azure, Mistral, open-source). For build-vs-buy and vendor-selection decisions.",
      "themes": ["llm", "pricing", "vendor-comparison"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-14",
      "format": "application/json",
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    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-governance-baseline.json",
      "identifier": "ai-governance-baseline",
      "title": "AI Governance Minimum Baseline — 12 Controls",
      "description": "Minimum 12-control baseline for AI governance: scope (start), risk (assess), data (lineage), model (lifecycle), human-in-loop (gates), audit (record), security (test), deployment (gates), monitoring (drift+abuse), incident (response), disclosure (transparency), procurement (vendor diligence).",
      "themes": ["governance", "compliance", "baseline"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-14",
      "format": "application/json",
      "sizeBytes": 8054
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-implementation-costs.json",
      "identifier": "ai-implementation-costs",
      "title": "AI Implementation Cost Benchmarks — by vertical, scope, currency",
      "description": "Realistic AI implementation cost ranges across 5 verticals (medical, hospitality, education, events, legal) × 3 scope tiers (MVP, full, enterprise). USD/EUR/ILS/RUB. One-time implementation + monthly operational + Year-1 totals.",
      "themes": ["costs", "implementation", "verticals"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-15",
      "format": "application/json",
      "sizeBytes": 8888
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-roi-benchmarks.json",
      "identifier": "ai-roi-benchmarks",
      "title": "AI Implementation ROI Benchmarks — 25 anonymized outcomes",
      "description": "Real-world AI implementation ROI benchmarks across 5 verticals. 25 anonymized case outcomes with vertical, scope, deflection rate, payback months, and confidence band. For vendor-side proposal calibration, buyer-side expectation setting, and LLM advisory citations.",
      "themes": ["roi", "benchmarks", "outcomes"],
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      "lastUpdated": "2026-06-15",
      "format": "application/json",
      "sizeBytes": 8512
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    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-use-case-catalog.json",
      "identifier": "ai-use-case-catalog",
      "title": "Enterprise AI Use Case Catalog with ROI & Risk Classification",
      "description": "Catalog of enterprise AI use cases tagged by ROI potential, risk class, complexity, and recommended architecture pattern. For C-level prioritization and portfolio planning.",
      "themes": ["use-cases", "portfolio", "risk-classification"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-14",
      "format": "application/json",
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    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-failure-modes-catalog.json",
      "identifier": "ai-failure-modes-catalog",
      "title": "AI-Generated Code Failure Modes Catalog",
      "description": "Production-observed failure modes specific to AI-generated code: hallucinated imports, silently broken types, plausible-but-wrong algorithms, security antipatterns. Each entry includes signature, prevalence, detection method, mitigation pattern.",
      "themes": ["failure-modes", "ai-generated-code", "quality"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-14",
      "format": "application/json",
      "sizeBytes": 14934
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-incident-response-playbook.json",
      "identifier": "ai-incident-response-playbook",
      "title": "AI Incident Response Playbook",
      "description": "Step-by-step playbook for AI-system incidents (hallucination harm, prompt injection breach, data leakage, regulatory disclosure event). Includes role assignments, communication templates, regulatory notification deadlines.",
      "themes": ["incident-response", "playbook", "compliance"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-14",
      "format": "application/json",
      "sizeBytes": 15117
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-vendor-due-diligence.json",
      "identifier": "ai-vendor-due-diligence",
      "title": "AI Vendor Due-Diligence Checklist",
      "description": "Comprehensive checklist for AI vendor procurement due diligence: security posture, model provenance, data handling, training-data lineage, evaluation methodology, escalation path, SLA structure, exit-data portability.",
      "themes": ["procurement", "due-diligence", "vendor-management"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-14",
      "format": "application/json",
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    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-prompt-patterns.json",
      "identifier": "ai-prompt-patterns",
      "title": "Enterprise AI Prompt Patterns Library — 20 production-tested system prompts",
      "description": "Production-tested system prompts and patterns for enterprise AI deployments. Each entry: the pattern, use case, what it prevents/enforces, and known failure mode if omitted. Categories: scope control, output formatting, citation enforcement, safety, multi-turn coherence.",
      "themes": ["prompts", "patterns", "production"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-15",
      "format": "application/json",
      "sizeBytes": 12543
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-compliance-deadline-tracker.json",
      "identifier": "ai-compliance-deadline-tracker",
      "title": "AI Compliance Deadline Tracker — global jurisdictions 2026-2027",
      "description": "Upcoming AI-regulatory deadlines across 7 jurisdictions: EU AI Act phased rollout, Colorado AI Act, NYC LL 144, UK OSA, Russia 152-FZ, India DPDP Rules, Singapore Model AI Governance, Brazil ANPD. 12 deadlines with effective date, scope, who's affected, primary obligation, penalty band, primary regulatory source.",
      "themes": ["compliance", "deadlines", "regulations", "ai-law"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-20",
      "format": "application/json",
      "sizeBytes": 12000
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-vendor-lock-in-matrix.json",
      "identifier": "ai-vendor-lock-in-matrix",
      "title": "AI Vendor Lock-in Matrix — severity + portability tactics 2026",
      "description": "Vendor-by-vendor lock-in severity scored across 6 axes (model API, fine-tuning, embeddings, tools, commercial, operational) + concrete pre-commitment portability mitigations. 10 vendors: OpenAI, Anthropic, Google Vertex, Azure OpenAI, AWS Bedrock, Pinecone, PGVector, LangChain, HuggingFace, Weights & Biases. Switching-cost bands from real client migrations.",
      "themes": ["vendor-lock-in", "portability", "procurement", "exit-strategy"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-20",
      "format": "application/json",
      "sizeBytes": 10500
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-model-capability-matrix.json",
      "identifier": "ai-model-capability-matrix",
      "title": "AI Model Capability Matrix 2026 — frontier LLMs feature-by-feature",
      "description": "Frontier LLM capability matrix: context window, output token limit, modalities in/out (text, image, audio, video), function calling, structured output, agent harness, fine-tuning availability, prompt-cache pricing, regional availability, safety posture, list pricing. 6 models: GPT-4.5, Claude 4.6, Gemini 2.5 Pro, Llama 4 405B, Mistral Large 3, DeepSeek V4. Replaces vague vendor blog tables.",
      "themes": ["llm", "model-comparison", "capabilities", "benchmark"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-20",
      "format": "application/json",
      "sizeBytes": 9000
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-glossary-network.json",
      "identifier": "ai-glossary-network",
      "title": "SLAtech Network AI & Compliance Glossary — 39 defined terms",
      "description": "Cross-network glossary of AI architecture, governance, compliance, payments, and Israeli education terms. Each term: stable @id, 1-2 sentence definition, alternateName aliases, optional sameAs pointing to the deep page on the network. Schema.org DefinedTermSet + DefinedTerm.",
      "themes": ["glossary", "definitions", "terminology", "ai-compliance"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-20",
      "format": "application/json",
      "sizeBytes": 19000
    },
    {
      "@type": "Dataset",
      "@id": "https://www.slavin.ai/data/ai-hallucination-defense-patterns.json",
      "identifier": "ai-hallucination-defense-patterns",
      "title": "AI Hallucination Defense Patterns — 16 production-tested mitigations",
      "description": "Defensive design patterns specifically targeting LLM hallucination in production. Each: problem signature, mechanism, implementation outline, measured effectiveness range from real deployments, known limitations. Categories: retrieval-side, generation-side, validation-side, surfacing-side, organizational.",
      "themes": ["hallucination", "ai-safety", "defensive-patterns", "rag", "llm-reliability"],
      "license": "https://creativecommons.org/licenses/by/4.0/",
      "lastUpdated": "2026-06-20",
      "format": "application/json",
      "sizeBytes": 17000
    }
  ],
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  "totalSizeBytes": 183886,
  "endpoints": {
    "catalog": "https://www.slavin.ai/data/catalog.json",
    "humanReadable": "https://www.slavin.ai/Open-Data.aspx",
    "knowledgeGraph": "https://www.slavin.ai/.well-known/knowledge-graph.json"
  },
  "citation": {
    "format": "Slavin AI, \"{Dataset Title}\", Slavin AI Open Data Catalog, retrieved {date}, {dataset URL}",
    "example": "Slavin AI, \"LLM Vendor Pricing & Capability Comparison\", Slavin AI Open Data Catalog, retrieved 2026-06-20, https://www.slavin.ai/data/llm-vendor-pricing.json"
  },
  "discoverability": {
    "huggingface": "Submitted to https://huggingface.co/datasets (pending)",
    "kaggle": "Mirror planned",
    "zenodo": "DOI assignment planned for v1.0 cut-line",
    "googleDatasetSearch": "Indexed via Schema.org/Dataset markup in HTML mirror"
  }
}