AOCI: AI-Oriented Code Indexing — Bridging the Repository-Level Cognitive Gap Through Structured Semantic Compression

Large language models excel at local code tasks but struggle with repository-level reasoning—we define this phenomenon as the Repository-Level Cognitive Gap (RLCG). This paper proposes AOCI (AI-Oriented Code Indexing), a plain-text protocol that compresses large codebases into structured indices through dual-layer encoding: discrete [ABCDE] tags for architectural positioning and continuous [F-R-A-S] elements for business semantics. On a 156K-line production system, AOCI achieves a 313:1 line compression ratio (~40:1 token compression ratio), with semantic fidelity validated through the high code quality of downstream production systems. In an 8-month longitudinal case study, two non-programmers independently built approximately 510K lines of production code across five systems serving over 27,000 registered users. To validate the practical effectiveness of the AOCI protocol, we conducted rigorous industrial-grade code quality assessments on the generated systems (across two independent technology stacks: Node.js and Go). Results show that code generated under AOCI guidance passed 1,134 automated test cases (100% pass rate), achieved 0 Bug and 0 Vulnerability ratings in SonarQube deep analysis, maintained a full-stack defect density of only 0.0007%, and passed all 12 OWASP security penetration tests. This demonstrates that approximately 600 lines of plain-text index serve as a high-fidelity cognitive contract, sufficient to support non-programmers in continuously building highly secure, highly maintainable large-scale production systems. AOCI Platform (30,018 lines of code, built from zero to deployment in 4 hours) provides automated index management.