What is local AI code review?

Local AI code review means reviewing and verifying AI-generated code inside your own environment — your machines, your network, your rules — without uploading source code to an external review service. The checks, the context, and the evidence stay where the code lives.

Who actually needs local AI code review?

Teams whose source code must not leave their environment: client code under NDA, proprietary algorithms, regulated industries, security-sensitive systems, and any organization whose policies rule out sending repositories to third-party services. For everyone else it is a trade-off; for these teams it is a requirement.

Does 'local' mean no cloud LLM at all?

Not necessarily — it is a spectrum. Fully local means models run on your hardware. Local-first means the workflow, context handling, and evidence live in your environment, while a model API may still be called under your own agreements and controls. What distinguishes both from a cloud review service is that no third party ingests your repository to operate the review.

Is local review as good as cloud review services?

Cloud services offer strong models with zero setup; that is a real advantage. Local approaches trade some convenience for control: code stays put, the workflow is auditable, and the setup works the same for every AI coding tool a team uses. Which side wins depends on your constraints — for code that cannot leave, the question answers itself.

How does this relate to GDPR and compliance requirements?

Keeping source code and workflow data in your own environment reduces the number of external processors involved, which many compliance-conscious teams prefer. That is an architectural property, not a certification — no tool makes you compliant by itself, and legal assessment of your specific setup stays with you.

Can I use local verification with Claude Code or Cursor?

Yes — that is the point of a tool-agnostic local layer. The AI coding tool generates changes as usual; the verification workflow around it (task boundaries, validation, evidence, human approval) runs locally and works the same regardless of which coding tool produced the change.

Concept

Local AI Code Review

Last updated: 2026-07-024 min read

Local AI code review means reviewing and verifying AI-generated code inside your own environment — without uploading source code to an external review service. The checks, the context, and the evidence stay where the code lives.

Contents

Why 'where does the code go?' became a review question

Most AI code review products are cloud services: they receive your pull request, analyze it on their infrastructure, and comment back. For many teams that is fine. For a substantial group it is a non-starter — not because of the review quality, but because of the first step: the code leaves.

Local AI code review exists for that group:

Agencies and consultancies working on client code under NDAs that forbid third-party processing.
Teams with proprietary core IP — the algorithm is the company; its source does not travel.
Regulated and security-sensitive industries — finance, health, defense, critical infrastructure — where vendor reviews and data-flow approvals make every new external processor expensive.
Compliance-conscious European teams that minimize external processing of internal artifacts on principle.

These teams still face the same verification debt as everyone else — they just cannot solve it by mailing their repository to a service.

What 'local' actually means — a spectrum, honestly

“Local” is used loosely in this space, so it is worth being precise. There are three levels:

The honest 'local' spectrum: what distinguishes the first two levels from a cloud service is that no third party ingests your repository to operate the review.

Fully local: models run on your own hardware. Maximum control, real operational cost, and model quality depends on what you can run.
Local-first: the workflow, repository access, context handling, validation, and evidence live in your environment; a model API may be called under your own agreements and controls. The repository itself is never ingested by a third-party review service.
Cloud service: a provider receives and processes your code to operate the review. Zero setup, strong models — and an external processor in your data flow.

Reality Graph sits in the second category: local-first by design. Precision matters here — a tool that calls a model API is not “fully offline”, and claiming otherwise would be marketing, not architecture.

Cloud review services vs. local verification

Both approaches have honest advantages:

Cloud review services win on setup time (minutes), model strength, and polished PR integration. If your code may leave and you want instant coverage, they are a reasonable choice.
Local verification wins on data control (code stays put), auditability (you can see and log exactly what ran), tool independence (one workflow across Claude Code, Cursor, Copilot, and whatever comes next), and procurement (no new external processor to approve).

The two also differ in scope: cloud reviewers read finished diffs, while a local verification layer can wrap the whole run — task boundaries before, validation and evidence after. That difference exists regardless of where either tool runs.

Setting up a local verification workflow

A workable local setup, tool by tool:

Keep generation where it is. Your team keeps using its AI coding tools — local review does not require replacing them.
Write the task down first. Goal, boundaries, validation plan — in the repo, next to the work, before the run.
Run deterministic checks locally. Tests, type checks, linters, build — your existing toolchain already runs in your environment; make its results part of every AI-assisted change.
Capture evidence per run. What was intended, changed, validated, skipped — stored with the code, not in a chat log.
Keep the human gate local too. Approval happens where the evidence is; nothing auto-commits.

Where Reality Graph fits

Reality Graph is a local-first verification layer built exactly for this setup: it structures the task before the run, checks boundaries, collects validation results, and produces a reviewable evidence report — in your environment, beside the AI coding tools you already use. It is in private beta; early access is open for a small group of teams.

What it does

Runs in your environment — local-first by design
One verification workflow across Claude Code, Cursor, Copilot, and similar tools
Evidence reports stored with your code, auditable by your team
Human approval gates — advisory by default, no auto-commit

What it does not do

Claim your setup is automatically GDPR- or audit-compliant — architecture helps, certification it is not
Replace your coding tools, tests, or CI
Upload your repository to a review service — that is the point
Make 'zero data ever leaves' promises — what leaves is determined by your configuration, visibly

FAQ

What is local AI code review?: Local AI code review means reviewing and verifying AI-generated code inside your own environment — your machines, your network, your rules — without uploading source code to an external review service. The checks, the context, and the evidence stay where the code lives.
Who actually needs local AI code review?: Teams whose source code must not leave their environment: client code under NDA, proprietary algorithms, regulated industries, security-sensitive systems, and any organization whose policies rule out sending repositories to third-party services. For everyone else it is a trade-off; for these teams it is a requirement.
Does 'local' mean no cloud LLM at all?: Not necessarily — it is a spectrum. Fully local means models run on your hardware. Local-first means the workflow, context handling, and evidence live in your environment, while a model API may still be called under your own agreements and controls. What distinguishes both from a cloud review service is that no third party ingests your repository to operate the review.
Is local review as good as cloud review services?: Cloud services offer strong models with zero setup; that is a real advantage. Local approaches trade some convenience for control: code stays put, the workflow is auditable, and the setup works the same for every AI coding tool a team uses. Which side wins depends on your constraints — for code that cannot leave, the question answers itself.
How does this relate to GDPR and compliance requirements?: Keeping source code and workflow data in your own environment reduces the number of external processors involved, which many compliance-conscious teams prefer. That is an architectural property, not a certification — no tool makes you compliant by itself, and legal assessment of your specific setup stays with you.
Can I use local verification with Claude Code or Cursor?: Yes — that is the point of a tool-agnostic local layer. The AI coding tool generates changes as usual; the verification workflow around it (task boundaries, validation, evidence, human approval) runs locally and works the same regardless of which coding tool produced the change.