3 posts tagged with "Architecture"

Every non-trivial code intelligence system faces the same fundamental question: How do you persist complex analysis results without sacrificing performance or flexibility? When we started building CodePrism's storage layer, we quickly realized this wasn't just about "saving data to disk"—it was about making architectural decisions that would shape the entire system's future.

This is the story of how we designed CodePrism's storage layer foundation: the decisions we made, the trade-offs we considered, and the patterns we chose to enable persistent code intelligence, written entirely in Rust with an AI-first approach.

Code quality isn't just about syntax—it's about the patterns that emerge from how we structure our software. A well-architected codebase follows recognizable patterns that make it maintainable, scalable, and robust. But identifying these patterns (and their problematic counterparts) across thousands of files requires more than human intuition. It requires AI-powered pattern detection.

CodePrism's detect_patterns tool doesn't just find design patterns from textbooks—it identifies the real-world architectural decisions that make or break software projects.

Most code analysis tools treat your codebase like a collection of isolated files. They parse each file into an Abstract Syntax Tree (AST), analyze it in isolation, and call it a day. But real software doesn't work in isolation—it's a web of interconnected relationships, dependencies, and data flows that span multiple files, modules, and even languages.

CodePrism takes a radically different approach: graph-based code analysis with a Universal AST. Instead of analyzing files in isolation, we build a unified graph representation of your entire codebase, enabling analysis that understands relationships, patterns, and behaviors that emerge at the system level.

Here's how we built an engine that can index 1000+ files per second and answer complex queries in sub-millisecond time.