Turn Dead SCORM Courses into a Living Knowledge Base

Enterprise training libraries contain decades of accumulated domain knowledge — safety procedures, compliance requirements, product specifications, onboarding processes. This content was created by subject matter experts at significant cost. But it's trapped inside SCORM packages that were designed for LMS interop, not for AI pipelines.

ScormParser bridges the gap. Our AI engine understands SCORM's internal structure, extracts every content asset, transcribes audio and video, and outputs pre-chunked content ready for embedding.

Upload a SCORM ZIP package via our API. ScormParser's AI processes the entire package — extracting text content, transcribing audio and video with speech-to-text, and structuring everything into clean Markdown or JSON. The output includes pre-computed chunk boundaries optimized for popular embedding models.

Different embedding models have different context windows and perform best with different chunk sizes. ScormParser lets you configure chunking strategies to match your model — whether you're using OpenAI's text-embedding-3-large, Cohere's embed-v3, or open-source models like BGE or E5. Each chunk includes course hierarchy metadata so your retrieval pipeline preserves context.

{
  "text": "All forklift operators must complete...",
  "metadata": {
    "course": "Warehouse Safety 2024",
    "module": "Equipment Operation",
    "slide": 7
  }
}

ScormParser's chunked output is designed for direct ingestion into popular vector databases. Load chunks straight into Pinecone, Weaviate, Qdrant, or ChromaDB without writing custom transformation code. The output format aligns with what these databases expect, so you can go from SCORM to searchable knowledge in minutes.