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NEXDGE
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/01  AI code review

Rules match.Review reasons.

The phrase means two things at once, and both matter here: review performed by an AI, and review of code an AI wrote. Nexdge exists at the intersection, and this page is about the category before it is about the product.

AI code review
AI code review is the use of a language model to read source code and report the problems in it: security vulnerabilities, logic errors, reliability risks and performance issues. It differs from traditional static analysis in that it reasons about what the code means rather than matching it against predefined rules, which lets it flag a problem nobody anticipated. The same property makes it non-deterministic: two runs over genuinely ambiguous code can reach different conclusions, which is why it complements a linter rather than replacing one.
/02  Context

Why thisis a problem.

Why generated code changed the problem

Code written by an AI assistant fails differently from code written by a tired human. It is idiomatic, consistently formatted, plausibly named and lint-clean, because those are the properties the training data rewarded. What it is not reliably is correct.

The mistakes that survive are semantic: a discount applied before tax instead of after, an authorisation check that validates the wrong identifier, a retry loop with no ceiling, a token that reaches a log line. None of those violate a rule. All of them look like working code in review, which is exactly the problem, because they also look like working code to the person who accepted the suggestion.

A rule engine cannot reach any of it, because the shape of the mistake is different every time it happens. That is the gap AI review is actually for.

How Nexdge structures a review

Five specialists run in parallel over the same code: Security, Reliability, Business Logic, Performance and Quality. Security and Reliability always run and are weighted more heavily in the final score than the other three.

Splitting the work is the point. A single reviewer looking at everything at once tends to lead with whatever is most visible, and what is most visible in code is almost always style. A specialist asked only about business logic does not get distracted by a variable name.

Every finding is rewritten, and the rewrite is scanned again before you see it. A deterministic pass runs alongside the specialists, so the parts of the review that can be checked mechanically are checked mechanically rather than reasoned about.

/03  Coverage

What the five specialists look for

  • Security

    Injection, broken authorisation, secrets in code or logs, unsafe deserialisation, weak crypto, and the paths that reach them.

  • Reliability

    Unhandled failures, missing timeouts, retry loops with no ceiling, resource leaks, and what happens when a dependency is slow rather than down.

  • Business logic

    Whether the code implements the rules you said it should. Ordering, rounding, edge cases at boundaries, and conditions that are inverted but syntactically fine.

  • Performance

    Queries inside loops, work repeated per request that could be done once, and structures that are correct at ten records and not at ten million.

  • Quality

    Readability, single responsibility, dead code, and error messages that will not help whoever reads them at three in the morning.

/04  How

Running a review

  1. Paste the code

    Open the review page and paste a file or a function. No repository connection, no OAuth grant and no install. Credits scale with line count, and anything under 100 lines costs one.

  2. Say what it is supposed to do

    Business context is optional and changes the result more than anything else on the page. Describe the rules the code implements in plain English, and the Business Logic specialist checks the implementation against them rather than guessing at intent.

  3. Read the severities, not the score

    The score is a summary. The finding list is the review. Severity runs from CRITICAL, meaning immediately exploitable, down to LOW, and PASS entries mark patterns that were implemented correctly.

  4. Take the rewrite

    Each finding comes with corrected code that has already been re-scanned. Read it rather than pasting it: it is a proposal from a system that cannot run your tests.

/05  Limits

What itdoes not do.

  • It is not deterministic. Two runs over genuinely ambiguous code can reason differently, which is why a linter and a test suite remain the things you gate a merge on.
  • It does not run your code. Nothing here executes, so a bug that only appears under real data or real concurrency can be missed entirely.
  • It cannot review architecture it cannot see. Given one file, it reasons about one file, and a design that is wrong across six services will not surface from any of them.
  • It can be confidently wrong. A finding is an argument to evaluate, not a verdict, and that is true of every tool in this category including this one.
  • It does not replace human review. It removes the mechanical part so a reviewer spends their attention on the part that needs judgement.
/06  Questions

The onesworth asking.

What is AI code review?
AI code review is the use of a language model to read source code and report problems in it, including security vulnerabilities, logic errors, reliability risks and performance issues. Unlike a linter or a static analyser, it reasons about what the code means rather than matching it against predefined rules, so it can flag a problem nobody wrote a rule for.
Is AI code review accurate?
It is useful and it is not infallible. It finds classes of problem that rule-based tools structurally cannot reach, and it can also produce a finding that is wrong, or miss one on a second pass that it caught on the first. Treat each finding as an argument to evaluate. Nexdge runs specialists at temperature zero and pairs them with a deterministic pass to narrow that variance, which reduces it rather than removing it.
Does AI code review replace human review?
No. It replaces the mechanical part of human review: the pass where somebody scans for the obvious and gets tired. Design decisions, whether a feature should exist, and whether an approach fits the system are judgement calls, and nothing here makes them.
Does it replace static analysis or a linter?
No, and running both is the correct setup. A linter is free, instant, offline and gives the same answer every time, which are four things AI review is not. It catches what a rule describes. AI review catches what no rule was written for. They fail differently, which is the reason to have both.
Is my code stored?
No. Code is processed in an ephemeral environment and destroyed at the end of the review. Nothing is written to disk and nothing is retained in our database.
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Three reviews free.
No card.

Paste a real file and read what five specialists say about it. About thirty seconds.

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