AI code review is software that uses an AI model to read every pull request as soon as it is opened, and to post comments on bugs, style problems and cases the developer missed. It also writes a short summary of the change for the human reviewer. A pull request is a proposed change to the code, which other developers examine before it joins the main code. The product is also called an AI code reviewer or automated code review.
Why is it needed? In most software teams, no change joins the main code until at least one other developer has read it. This rule catches mistakes early, but it is slow. A reviewer may have ten pull requests waiting, each changing hundreds of lines across many files. With AI coding assistants and agents, developers now write code faster, so even more changes wait for review.
Here is how it works. The team connects the review tool to its code repository, the system that stores the code and its history. Each time a pull request is opened, the repository sends it to the tool. The tool reads the diff, which is the list of lines added and removed. It also reads the files around the change, so that it knows what the changed code depends on. A language model then writes comments, each attached to one line, as a human reviewer would. The developer can reply to a comment, and the tool answers.
Take, for example, a SaaS start-up in Chennai that sells invoicing software to small businesses in other countries. A developer opens a pull request that adds late-payment fees: 180 lines in four files. Two minutes later, the AI reviewer posts a three-sentence summary and five comments. One comment says that the fee is calculated before the due date is checked, so an invoice paid on time could still be charged a fee. Another says that a negative amount, which the software uses for credit notes, is not handled. A third suggests a clearer name for a function. The developer fixes the first two, and replies that the third name follows the team's own standard. A senior developer then reads the change, with the summary and the comments in front of her, and approves it.
The AI reviewer does not approve or merge the change. Merging means adding the change to the main code. In most teams, the repository is set up so that a merge needs a human approval. The AI can also be wrong. It can object to a line that is correct, and it can miss a bug that depends on how the business works. So its comments are suggestions, which the developer accepts or rejects.
AI code review sits beside two other checks. The CI/CD pipeline builds the code and runs the team's automated tests. Static application security testing (SAST) searches the code for known patterns of security flaws, using fixed rules. AI code review reads the code more as a person does. So it can notice that the code does not do what the pull request says it does. It differs from AI code governance, which checks AI suggestions inside the editor, before a pull request exists.