Enola AI Tool Uses Typed Fact Model to Extract Reliable Architectural Insights

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Enola is an AI-assisted architectural analysis tool being developed to extract structured facts from source code before any AI reasoning begins. Rather than treating all relationships as generic graph edges, Enola uses a typed fact model that distinguishes between function calls, imports, route registrations, and type references. The system separates raw parsing from architectural extraction, so that framework-specific patterns — such as composed router paths in Go or Spring annotations — are correctly interpreted into meaningful facts. Repositories serve as independently addressable extraction scopes, meaning Enola can reason about a codebase even without loading its dependencies. Because architectural boundaries rarely align with source ownership boundaries, Enola assigns each entity a scoped identity that includes context like language, qualified name, and source revision.

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