Local AI Code Review

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.

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

1. Fully local: models run on your own hardware. Maximum control, real operational cost, and model quality depends on what you can run.
2. Local-first: the workflow orchestration and selected evidence storage live in your environment; a model API or external tool may still receive configured inputs. Record repository access, transmitted context, telemetry, logs, subprocesses, and retention separately.
3. Cloud service: a provider receives and processes your code to operate the review. Setup and capabilities depend on the provider, and the provider becomes part of the documented 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.

Both approaches require an architecture-specific assessment:

• Cloud review services may reduce integration work and offer hosted models or PR integration. Data inputs, retention, telemetry, controls, and coverage vary by provider and configuration.
• Local verification can keep selected checks and evidence in an operator-controlled environment. That does not make every dependency local or every action observable; model calls, plugins, CI, logs, and storage still require a component-by-component review.

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.

A workable local setup, tool by tool:

1. Keep generation where it is. Your team keeps using its AI coding tools - local review does not require replacing them.
2. Write the task down first. Goal, boundaries, validation plan - in the repo, next to the work, before the run.
3. 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.
4. Capture evidence per run. What was intended, changed, validated, skipped - stored with the code, not in a chat log.
5. Keep the human gate local too. Approval happens where the evidence is; nothing auto-commits.

If you also want a locally hosted model to carry part of the review itself, the local LLM code review setup guide covers hardware sizing, model selection, and the concrete workflow.

Reality Graph is designed as a local-first workflow layer: it can structure task boundaries, record configured validation results, and produce a reviewable evidence report in the operator's environment. Those records describe observed workflow events; they do not prove a complete data path, correctness, confidentiality, security, or compliance. Which parts of a run stay on the machine, and the cases where something does leave it, are set out in what leaves the machine and what does not. It is in private beta; early access is open for a small group of teams.