{
  "version": 1,
  "offer": {
    "slug": "embedding-studio-free-credit",
    "url": "https://that.promo/embedding-studio-free-credit",
    "json_url": "https://that.promo/embedding-studio-free-credit.json",
    "name": "Embedding Studio $10 free credit",
    "provider": "Embedding Studio",
    "company": "embedding-studio",
    "company_url": "https://that.promo/embedding-studio",
    "category": null,
    "category_url": null,
    "program": null,
    "status": "live",
    "headline_amount": null,
    "headline_note": null,
    "value": "$10 of trial credit, no card",
    "summary": "Embedding Studio fits and calibrates a small model on your own labeled past cases and serves it as an API or a downloadable calibration file, routing low-confidence decisions to a person. New workspaces start with $10 of trial credit, no card needed.",
    "who_qualifies": null,
    "how_to_claim": null,
    "expiry": "ongoing",
    "caveats": "Trial credit has no cash value and is not refundable. Fitting and calibration are free today; only decisions spend credit.",
    "why_it_matters": null,
    "official_url": "https://studio.embedding.in/",
    "released_at": null,
    "last_verified": null,
    "added_at": "2026-11-01T09:02:15Z",
    "updated_at": "2026-11-01T09:03:11Z"
  },
  "events": [
    {
      "kind": "changed",
      "at": "2026-11-01T09:03:11Z",
      "note": "Removed the decisions count from value, summary and caveats: the homepage and docs disagree (5,000 vs 10,000), so only the $10 credit is listed.",
      "source_url": "https://studio.embedding.in/"
    },
    {
      "kind": "new",
      "at": "2026-11-01T09:02:15Z",
      "note": null,
      "source_url": "https://x.com/Nandakishorm1/status/2115486146897781240"
    }
  ],
  "posts": [
    {
      "source": "x",
      "url": "https://x.com/Nandakishorm1/status/2115486146897781240",
      "author_name": "Nandakishor m",
      "author_handle": "Nandakishorm1",
      "text": "We are launching Embedding Studio. You guys asked for a platform to fit your data, calibrate, and create api out of it, or download a calibration file to fit locally. Here you go.\nAfter fitting and calibration, you set a threshold; the model will say nothing if it doesn't have any good confidence in the outcomes, or route it to a human if it has lesser confidence just route to a person.\nIt also works out of the box\nThere is free credits\n\nhttps://studio.embedding.in/",
      "posted_at": "2026-10-28T16:49:35Z"
    }
  ]
}