StoryWeave AI

StoryWeave AI — an AI-powered story ideation platform that transforms raw ideas into structured narratives using RAG + NVIDIA NIM models for outline, beat, and scene generation.

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Inspiration

Storytelling is both art and structure — yet most AI tools focus only on raw text generation.
We wanted to build something that thinks like a writer’s room assistant, not just a chatbot.
StoryWeave AI was born from our desire to merge creativity with computation — a space where large language models collaborate with humans to shape ideas into structured, cinematic stories.

What it does

StoryWeave AI turns a single creative idea into a full story outline and detailed scenes.
It:

  • Ingests worldbuilding or concept Markdown files
  • Generates story beats and arcs using NVIDIA NIM LLMs
  • Expands scenes with consistent tone and pacing
  • Exports the entire story draft as a .txt file ready for editing

In essence, it transforms raw imagination into a structured narrative pipeline.

How we built it

  • Backend: FastAPI (Python) handling orchestration, retrieval, and routing
  • Frontend: Lightweight HTML/JS interface served from static/index.html
  • AI Core: NVIDIA NIM inference endpoints for text and embedding models
  • Architecture: Modular RAG pipeline with semantic retrieval over Markdown corpus
  • Integration: Dockerized for reproducibility, configurable via .nim.env
  • Optional Mock Mode: USE_MOCK=1 enables full local testing without API keys

Challenges we ran into

  • Aligning creative freedom with factual Markdown grounding
  • Handling secure and persistent NVIDIA API integration
  • Building a responsive UX that hides backend complexity
  • Managing multiple model endpoints while keeping latency low

Accomplishments that we're proud of

  • Working end-to-end prototype connecting ingestion → outline → expansion → export
  • Seamless NVIDIA Cloud + local FastAPI integration
  • Clear documentation and reproducible environment
  • Architecture flexible enough to extend toward agentic workflows

What we learned

  • AI storytelling improves dramatically when guided by context-rich retrieval
  • NVIDIA NIM APIs make enterprise-grade AI accessible for lightweight prototypes
  • Iterative generation (beats → scenes → exports) helps maintain narrative quality
  • Team coordination and modular design are key for scalable agent systems

What's next for StoryWeave AI

  • Multi-user collaboration with live editing
  • Character and tone management dashboards
  • Extended export options (PDF, screenplay format, etc.)
  • AWS Amplify or Hugging Face deployment for public demo access
  • Integration of RAG-based feedback refinement loops
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