Ortho targets AI coding gaps with structured workflow and smarter context management

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AI coding tools excel at generating code quickly but lack workflow structure and context discipline, leading to architectural issues in large or complex codebases. A platform called Ortho is being developed to address these shortcomings through two core systems: ASES, a six-phase AI development methodology, and a nine-component token optimization pipeline. ASES breaks AI-assisted development into sequential phases — planner, architect, builder, test-designer, verifier, and reviewer — each handled by a separate agent call, mimicking the structured process used with human engineers. The system also stores rejected decisions so the AI does not repeatedly propose the same discarded patterns in future tasks. On the context side, Ortho aims to send only relevant code as input rather than flooding the model with noise like boilerplate or vendored files, which the developers argue degrades reasoning quality.

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