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The Context Engine

How raw context becomes portable knowledge — at 98.77% lower cost

Reasoner Context Engine v9
Input
Output
01
Raw Context Documents, PDFs, transcripts
10.7M tokens
02
Context Lens Query decomposed, rewritten into a precision lens
Ambiguity removed
03
Context Illumination Only relevant context lights up through the lens
Irrelevant filtered out
04
Context Decomposition Relevant context broken into atomic units
Constraints extracted
05
Neurosymbolic Encoding Structured knowledge representation
80× compression
06
Reasoner Core Portable across any LLM
350 tokens · 99.33%
10.7M → 3,270 tokens 98.77% cost reduction
Zero variance between runs Deterministic encoding
Any model, any device 350 tokens per core
Persists across sessions Knowledge compounds