{
  "slug": "goat",
  "name": "GOAT",
  "description": "GOAT Labs measures how AI in production actually performs by operating the largest opt-in corpus of production LLM telemetry. They provide this data to frontier labs and enterprises for research and performance analysis, offering cashback to teams that contribute their telemetry. They also conduct live, real-world benchmarks of frontier AIs.",
  "url": "https://optimly.ai/brand/goat",
  "websiteUrl": "https://goat.ai/",
  "logoUrl": "https://logo.clearbit.com/goat.ai",
  "baiScore": 49,
  "bai_tier_status": "active",
  "bai_score_status": "active",
  "archetype": "Challenger",
  "archetype_status": "active",
  "category": "AI Performance & Research",
  "categorySlug": null,
  "keyFacts": [],
  "aiReadiness": [],
  "competitors": [],
  "competitorsProse": null,
  "inboundCompetitors": [],
  "aiAlternatives": [],
  "parentBrand": null,
  "subBrands": [],
  "updatedAt": "2026-08-09T00:02:09.567Z",
  "verifiedVitals": {
    "website": "https://goat.ai",
    "founded": "Not mentioned.",
    "headquarters": "Not mentioned.",
    "pricing_model": "Subscription model for access to studies and the corpus; cashback incentive model for data contributors.",
    "core_products": "Production LLM telemetry corpus, AI performance studies and research papers, live AI benchmarks (e.g., Polymarket benchmark), data contribution platform (with cashback for token spend).",
    "key_differentiator": "Operates the largest opt-in corpus of *production* LLM telemetry, offering real-world performance data. Provides a unique cashback incentive for data contribution. Conducts pioneering live AI benchmarking on prediction markets, moving beyond synthetic tests.",
    "target_markets": "Frontier AI labs, enterprises developing and deploying LLMs in production, universities and research institutions.",
    "employee_count": "Not mentioned.",
    "funding_stage": "Not mentioned.",
    "subcategory": "LLM Telemetry and Benchmarking"
  },
  "intentTags": {
    "problemIntents": [
      "In-house Manual Logging & Analysis: Companies could manually log LLM inputs, outputs, and internal states from their production environments and then develop custom scripts and dashboards for analysis",
      "Relying on Synthetic Benchmarks: Teams might continue to rely solely on public synthetic benchmarks (e.g., MMLU, HumanEval) or internal small-scale tests, foregoing real-world production performance i",
      "AI Performance Consulting Firms: Engaging third-party consultants or agencies specializing in AI model evaluation and auditing. This can provide expertise but typically lacks the continuous, large-sca"
    ],
    "solutionIntents": [
      "GOAT Labs AI performance measurement",
      "LLM telemetry data",
      "AI model benchmarking",
      "production AI data for research",
      "AI research cashback token spend",
      "General MLOps Platforms: Utilizing broader MLOps platforms (e.g., MLflow, ClearML) that offer experiment tracking and general model monitoring, but may lack the specialized, in-depth LLM telemetry col"
    ],
    "evaluationIntents": []
  },
  "businessProfileClaims": [],
  "timestamp": 1786526704635
}