AI Assignment Grader
Automated code review against company standards
Engineering teams spent hours manually reviewing code submissions. Inconsistent feedback missed pattern violations while good practices went unrecognized. Quality standards were subjective.
The client needed to evaluate code submissions at scale—whether from new hires, contractors, or training programs. Manual review was slow and inconsistent. Different reviewers flagged different issues. Anti-patterns slipped through while good code was sometimes criticized unfairly. There was no systematic way to grade code against the company's actual codebase patterns and established conventions.
Codebase Pattern Extraction
AI analyzes the company's existing codebase to learn established patterns, naming conventions, architectural decisions, and coding standards—creating a living style guide from real code.
Semantic Code Understanding
LLM-powered embeddings capture the intent and structure of submitted code. The system understands that different implementations can achieve the same goal correctly.
Pattern Matching & Anti-Pattern Detection
Submissions are compared against learned patterns. The system identifies deviations, flags anti-patterns, and recognizes when submissions follow best practices—even with different syntax.
Contextual Feedback Generation
Detailed, actionable feedback explains why code does or doesn't align with company standards. References to similar patterns in the actual codebase help developers learn.
90% reduction in time spent on initial code review and grading.
Consistent evaluation criteria applied across all submissions.
Anti-patterns flagged automatically with specific improvement suggestions.
New developers learn company patterns faster through contextual feedback.
Senior engineers focus on architecture decisions, not style enforcement.
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