Baz AI Code Review is an AI-native code review platform designed to help engineering teams ship higher-quality code faster. Unlike traditional static analysis tools or AI reviewers that focus on style and syntax, Baz performs deep, behavior-aware code analysis to catch real bugs, breaking changes, and contract violations before they reach production. Baz understands how code works, not just how it looks. Deep, behavior-focused code analysis Baz analyzes AST diffs, control flow, APIs, and usage patterns to identify issues that matter in real systems. It detects breaking changes such as renamed or removed APIs, behavioral regressions, unsafe assumptions, and edge cases that are easy to miss during manual reviews. This allows teams to catch production-impacting issues early, without slowing development. Spec-aware reviews tied to real requirements Baz connects code changes directly to product intent. By validating changes against Linear tickets and specifications, Baz ensures that what was implemented matches what was requested. This reduces misalignment between product and engineering, shortens feedback cycles, and prevents incomplete or incorrect implementations from slipping through review. Customizable AI agents for every team Every engineering organization has its own conventions, architecture, and risk profile. Baz allows teams to define custom AI review agents that enforce internal coding standards, security policies, infrastructure rules, and domain-specific best practices. This makes Baz effective across multiple teams and disciplines, including backend, frontend, data engineering, and SRE. Faster reviews without human friction Baz significantly reduces code review time by automating repetitive and high-signal feedback. Because feedback comes from an AI reviewer rather than a teammate, it helps remove interpersonal friction while maintaining consistent standards across teams, locations, and seniority levels. Built for real-world engineering workflows Baz integrates directly into existing change workflows and scales to large, multi-file changes. Teams can tune sensitivity to reduce noise, focus on high-impact issues, and gradually expand coverage as their codebase evolves.