01
Ingest
File ParserUnstructured data (PDF, PPTX, DOCX) is parsed into structured text blocks.
Architecture Comparison
Architecture: Deterministic Compiler vs. Probabilistic AI
// WHY JUDGMENT ≠ GENERATION
The greatest systemic risk in modern venture capital is conflating narrative generation with structural judgment.
Legacy AI tools (ChatGPT, Claude, and their respective wrappers) are probabilistic generation engines. They excel at formatting pitch decks and summarizing data rooms. LLMs optimize for persuasion — they applaud a well-written narrative, but they cannot evaluate mathematical business physics.
askOdin is a deterministic physics engine. We do not generate text; we compile logic.
The Doctrine
LLMs optimize for persuasion.
askOdin compiles for physics.
The askOdin Protocol Stack · U.S. Prov. Patent 63/948,559
The RUNE Protocol™ Architecture
Extraction is LLM. Evaluation is Go. Separation is the audit trail.
02
Extract
Probabilistic IsolationWe utilize Tier-3 LLMs strictly for extraction, isolating typed claims (TAM, Unit Economics, Headcount) without allowing the model to evaluate them.
03
Compile
Deterministic Go EngineThe extracted claims are routed entirely outside the neural network. A statically-typed Go engine mathematically evaluates the variables against the askOdin Judgment Graph™. This is where terminal physics violations (e.g., unit economics that mathematically cannot scale) are flagged.
04
Score
The Clarity Framework AggregatorThe engine outputs a 40-dimensional verdict, culminating in the Clarity Score™ (0–100).
Competitive Physics
The LLM Commoditization Reality
When generic LLMs become faster and cheaper, probabilistic AI startups face an existential threat. For askOdin, it simply reduces our extraction cost floor.
LLMs give you inference. askOdin gives you a benchmark universe. An LLM can generate a plausible verdict, but it cannot issue a reproducible, auditable score that an LP trusts across funds and across years.
RUNE isn't the model.
It's the compiler.