WhatA platform and workflow toolkit that helps writers use LLMs to co-create fiction through structured world bibles, style guides, and iterative polish processes — producing publishable-quality AI-assisted stories.
SignalThe author demonstrated that with significant upfront structure (world bibles, style guides, markdown docs similar to agentic coding) and weeks of polish to remove LLM-isms, AI can produce fiction that genuinely moves readers emotionally — but the process is still ad hoc and requires deep prompt engineering expertise.
Why NowClaude and GPT-4 class models just crossed the quality threshold where their fiction output is good enough to polish into publishable work, but no tooling exists to systematize the structured worldbuilding-to-polish pipeline.
MarketSelf-published authors, content studios, and indie game narrative designers; ~$5B self-publishing market growing 15%+ annually; competitors like Sudowrite focus on drafting, not the full structured worldbuilding-to-polish pipeline.
MoatAccumulated library of proven world bible templates, style guide frameworks, and de-LLM-ification heuristics that improve with each project — a compounding process knowledge advantage.
AI-Powered Rocket Design Optimization PlatformP5/10A cloud-based platform that uses AI agents to iteratively design, simulate, and optimize amateur and commercial rocket configurations with structural integrity analysis included.
STEM Project Kit Platform for Homeschool KidsC6/10A subscription service delivering structured, hands-on engineering projects (rocketry, electronics, robotics) with progressive difficulty for project-oriented learners aged 8-14.
Unified Drone Design and Flight SimulatorC5/10An open-source or freemium CAD-to-simulation tool for designing custom drones, testing aerodynamics, and virtually flying them before building.
White-Glove Custom Model Training for Mid-Market CompaniesP6/10A managed service that handles the full lifecycle of custom AI model training — from data preparation through fine-tuning and RL alignment — for companies that lack in-house ML teams.