Super Intelligence Optimization (SIO v1.0): Algorithmic Discovery Protocols for Autonomous B2B SaaS Web Agents

Modern B2B SaaS procurement has shifted from traditional human-led search to autonomous agent discovery. Buyers increasingly rely on Large Language Models (LLMs) such as ChatGPT Search, Perplexity, and Claude to execute vendor research, synthesize feature comparisons, and calculate ROI. Super Intelligence Optimization (SIO) is an evidence-led operating framework engineered to connect traditional search architecture with the token-constrained ingestion protocols of answering engines. This paper defines the baseline SIO architecture required to transition enterprise web platforms from human-only indexing to autonomous agent discovery, maximizing Share of Model (SoM). Canonical Entity & Architecture: https://rakesh.work/