AI generates code fast, like a wild horse.
LiveReview tames it into your workhorse.
AI Speed
- Raw power.
- Immense potential.
- Uncontrolled behavior.
AfterAfter LiveReviewHuman Control
- Guided.
- Focused.
- Reliable.
Blast-Radius Aware AI Code Review
for Business-Critical Systems.
Your team's attention is limited. Spend review effort where business risk is highest — not spread evenly across every diff.
LiveReview scores risk using a live call graph of your codebase, not just the diff in front of you. Hover a node to preview its blast radius, click to lock it in.
82 symbols from LiveReview's own codebase — size shows how central a symbol is.
How does Blast Radius scoring work? (a more technical explanation)▾
Here's the goal:
- A 3-line fix in a function used by 40 other files, that also writes to a database, should score high.
- A 300-line UI change in one file, fully covered by tests and used by nothing else, should score low, even though it's the bigger diff.
To get there, LiveReview gives each hunk two scores, then combines them into one and ranks every hunk in the diff by it.
- Blast Radius: how far a change can reach through your code.
- How many other places call this code, directly or a few steps removed
- Whether it writes to a database or other long-term storage
- Whether those callers live in other parts of the codebase, not just nearby files
- Review Priority: how much scrutiny a change warrants, based on its complexity, subtlety, and potential for important details to be missed.
- How many different paths the logic can take, and how hard it is to follow
- How deeply loops sit nested inside other loops
- How many other functions or symbols this code itself calls into
- Whether the code has tests
LiveReview may add new signals over time. The two questions behind them stay the same: how far, and how much scrutiny.
Customer trust runs on uptime, responsiveness, and delight — all of which demand engineering confidence.
LiveReview is the org-wide harness for AI-written code — turning raw AI power into enterprise-grade dependability.
Enforce your standards on every line of AI-generated code.
Activate checks at the levels that matter to you — mix and match enforcement per repository.
Want the code nobody committed to today still checked? See Scheduled Reviews below.
Pick the Right Review Depth Based on Your Need for Shipping Speed
Not every repo needs the same amount of scrutiny. Turn on more checkpoints where the blast radius of a bad change is high, and fewer where speed matters most.
- •Commit Only: fastest, lightest net. Fine for low-stakes, throwaway repos.
- •Commit + Scheduled: the startup pick. Near-zero friction day-to-day, plus a daily sweep that catches anything that slipped past commit-time checks.
- •Commit + Before Push + MR/PR: the standard team flow. A human sees every change before it merges.
- •All Five (Full Harness): maximum coverage, for repos where a bad change is expensive: payments, auth, core infra.
There's no single right answer, only the right trade-off for a given repository, team, or organization. Mix and match per repository, and change your mind any time.
Prevent Outages, Breaches, and Technical Debt Before They Happen
Every commit git-lrc reviews gets checked against the same risk categories LiveReview tracks across production codebases. Hover or tap any item below to see exactly what it costs when it slips through.
What takes down production - and impacts your on-call rotation.
Reliability
10 risks tracked
Correctness
10 risks tracked
Performance
10 risks tracked
Scalability
10 risks tracked
Reliability → Error Handling
Unhandled errors crash services mid-request, leaving customers staring at broken pages during peak traffic.
👋 Hi, I'm Livi. Ask me anything about your engineering data.
An AI chatbot for your engineering data
Ask Livi a product, engineering, or ops question in plain English. It answers with a chart pulled straight from your organization's own data, no dashboards to build, no SQL to write.
Every engineering decision becomes more data-backed, so you can act with confidence instead of guesswork.
Exec
Track adoption and ROI across the whole org without waiting on a report.
“How broadly has the org adopted LiveReview this month?”
Eng Manager
See which repos and engineers need attention, and where issues are piling up.
“Which repos have the highest issue concentration?”
Product
Understand engineering velocity and capacity by repo, in plain language.
“Which repos are gaining or losing engineering velocity?”
Engineer
Trace an incident back to the reviews and files that touched it.
“Which files have the most issues flagged?”
16 example questions across 8 categories, the same structure as LiveReview's Onboarding Report. Use ← → to browse.
Get Actionable Engineering Intelligence with MCP and APIs
Every code review performed by LiveReview adds to a growing source of engineering intelligence. Query, analyze, and act on that knowledge with the LiveReview MCP and API.
Instead of manually piecing together pull requests, comments, and reviews, generate custom reports, identify your strongest contributors, uncover quality and security trends, drill into engineering activity, or ask whatever your imagination comes up with, all in minutes instead of hours.
- 1Go to CI/CD Gates in the LiveReview dashboard and click 'New Ruleset'.
- 2Name it, then write or pick a preset jq expression against the findings document.
- 3Use 'Ask LLM' if the condition gets complex: it builds a copy-paste prompt (with your org's taxonomy and a sample document) for ChatGPT, Gemini, or DeepSeek to draft the expression.
- 4Confirm the live BLOCK/ALLOW preview matches what you expect against a real past review, then save.
- 5Open the ruleset's 'Get code' integration page, copy the snippet for your CI provider, and add the LIVEREVIEW_API_KEY secret to your pipeline.
…and many more.
Enforce Your Team's Engineering Standards with Repository Rules
A good reviewer doesn't just know your language and framework — it knows your repository: which patterns your team prefers, which dependencies are off-limits, and which files don't need a second look. LiveReview enforces your team's engineering standards through repository rules — drop a .lrc/ directory in your repo and LiveReview reads it on every review.
This is per-repo, and stacks on top of any org-wide Custom Prompts. Most teams should start with Repository Rules — each team keeps its own rules without affecting anyone else's repo.
.lrc/
├── ignore # files the reviewer never sees
├── rules/
│ ├── INSTRUCTIONS.md # read first, every review
│ ├── security.md
│ └── style.md
└── policy/
└── tools.toml # which checks are allowed to runRepository Rules
Write down the handful of decisions that come up in every review — "prefer direct SQL over ORM abstractions", "avoid new infrastructure dependencies". INSTRUCTIONS.md is read first, every other rules/*.md file follows in order, so the reviewer always has your team's context.
Ignore File
Point the reviewer away from generated code, vendored dependencies, and anything else that doesn't need a second look. Gitignore syntax, matched from your repo root — ignored files don't count toward billable lines.
PoliciesComing soon
Decide which tools and checks are allowed to run on this repo. Machine-readable settings that LiveReview reads directly — never sent to the AI model.
Static ChecksComing soon
Pair AI review with static analyzers like semgrep and eslint, authorized through policy and run as part of the same commit-time flow — one gate, multiple kinds of checks.
CI/CD Gates: Precise, Customized Merge Enforcement
Write a jq rule against a review's findings, save it, and call one URL from any CI/CD pipeline to block or allow the build. A generic "fail if severity is high" checkbox can't match how your team actually thinks about risk, jq can.
- Works on any review source: web, git-lrc, CI/CD triggers, or API/MCP triggers all produce the same findings document, so one gate covers every path to a merge.
- Full power of jq to enforce complex conditions, customized exactly to repository, team, and org requirements.
- Live expression editor: LLM help drafts a jq rule, live preview runs it against a synthetic sample and real past reviews as you type.
Write the rule that matches how your team actually weighs risk
Different teams weigh the same findings differently. jq lets each team encode its own answer, not a preset severity bucket:
Security-conscious team
Even one security finding is unacceptable, and so is any critical-severity bug elsewhere.
Block if there's any security finding, or any critical-severity finding of any kind.
(.counts.by_category.security > 0) or (.counts.by_severity.critical > 0)Fast-moving startup
Ship velocity matters most: block only on correctness and UX bugs that would actually hurt users or slow the team down, let architecture/style debt through for now.
Block only if there's a critical or warning-level finding in correctness, UX, or developer experience.
[.findings[] | select((.category=="correctness" or .category=="ui-ux" or .category=="developer-experience") and (.severity=="critical" or .severity=="warning"))] | length > 0Mature enterprise
Security is non-negotiable, but cost, scale, and architecture decisions matter just as much once you're operating at scale.
Block on any security finding, or on a critical finding in cost, architecture, or scalability.
(.counts.by_category.security > 0) or ([.findings[] | select((.category=="cost" or .category=="architecture" or .category=="scalability") and .severity=="critical")] | length > 0)Every category above comes from the same 10-category, 100+ risk taxonomy LiveReview already tracks on every review: the gate is just a rule over data you're already generating.
Cut AI Review Costs by 50% Without Compromising Quality
Adaptive Reviews saves 40-50% of AI inference costs using an innovative multi-model technique. A powerful Leader Model detects complex issues, while a cost-efficient Helper Model explains them - delivering the exact same review quality at half the price.
Reduce Costs by 40-50%
Significantly lower your AI inference costs. By using a cost-efficient Helper Model for explanations, you can save 40-50% compared to using a single expensive model for everything.
Double Review Volume
Review up to 2x more code with the exact same budget. With optimized token usage, you don't have to constantly monitor limits or restrict how much code gets reviewed.
Leader + Helper Architecture
A powerful Leader Model is solely dedicated to analyzing your code and finding complex issues, while a more economical Helper Model expands those findings into helpful, detailed explanations.
Maintain Review Quality
Because issue detection is still driven by the high-end Leader Model, there is no degradation in the quality or accuracy of the code reviews you receive.
Cognitive Coverage via PR Quizzes
AI can write a diff faster than you can read it. LiveReview quizzes you on the PR before it gets committed — so you stay in touch with the technical decisions baked into your codebase, instead of rubber-stamping code you never really understood.
AI Code Review with Teeth.
A powerful and flexible AI code reviewer — light on the pocket, heavy on impact.
No setup required — start reviewing code in minutes
See What LiveReview Delivers
Real benefits that engineering teams experience every day
Business Impact
Why Engineering Teams Love LiveReview
Accelerate Delivery Cycles
Reduce PR review time from hours to minutes, enabling your team to ship features faster and with greater confidence
Save Senior Engineering Time
Liberate senior developers from routine reviews, allowing them to focus on mentorship and high-impact architectural work
Drive Quality Excellence
Build a culture of quality with metrics that highlight improvements in code standards, reduced defects, and development efficiency
Used by engineers at
Powerful Features for Modern Engineering Teams
Everything you need to transform your code review process
Track Engineering Excellence
Quantify your team's improvement with comprehensive metrics. Track review times, code quality trends, and team velocity to demonstrate engineering value to stakeholders.
Fine-Tuned LiveReview AI Model
Live Review comes with its own fine-tuned AI model ready to use from day one. If you prefer to use your own provider — Gemini, OpenAI, AWS Bedrock, a self-hosted Ollama model, or any other LLM — you can bring your own key (BYOK) and plug it in.
Use Any git Provider: Github, GitLab, Bitbucket, Gitea, Azure DevOps
Works effortlessly with GitHub, GitLab, Bitbucket, Gitea, Azure DevOps. Connect your repositories in minutes and start receiving AI-powered code reviews across all your projects.
View All AI Reviews in One Place
Manage all your code reviews from a single, intuitive interface. Track review status, prioritize PRs, and monitor team activity with real-time updates.
Look Under the Hood with Detailed Progress Tracking
Monitor review progress in real-time. Track which files have been reviewed, identify bottlenecks, and ensure nothing falls through the cracks.
Customize Review Prompts to Fit Your Team
Custom Prompts apply org-wide, to every repo in your organization, so keep them to standards that are genuinely universal. For anything specific to one repo, use Repository Rules instead — most teams should start there.
Discuss with AI in MR and See it Learn Everyday
Build an institutional knowledge base from code reviews. Capture best practices, common issues, and team learnings to continuously improve code quality.
Sharp AI-Generated Pull Request Summaries
Get detailed, actionable summaries of every pull request. Understand changes at a glance with AI-generated insights that highlight key modifications, potential issues, and improvement suggestions.
Ask AI for Clarification or Debate Code Changes
Ask questions and get instant clarifications about code changes. The AI reviewer understands context and provides helpful explanations to speed up the review process.
Scheduled Reviews: A Safety Net for the Code Nobody Reviewed
Not every change goes through a full review. A hotfix might land straight on the main branch, or a dependency bump might merge on its own. For a fast-moving startup team, that's often the right call, so Scheduled Reviews close the gap left behind.
- •Checks your default branch on its own schedule, even when nobody asked for a review
- •Turn it on per repository, one toggle
- •Pick how often it runs, in plain cron syntax, or leave it blank for once a day
- •Shows the last run and the next run, right in the schedule list
Two CLI tools. One LiveReview backend.
Install git-lrc for commit-time reviews in any terminal. Use claude-lrc when you're building inside Claude Code. Both tools share the same AI review engine and monthly LOC quota.
git-lrc
Hooks into git commit and runs a micro AI review before every diff lands. Skips are explicit and auditable in git history.
- Git-native — works in any repo without a cloud platform connection
- Skips tracked in git log — auditable, not silent
- One-line install, 30k LOC free every month
claude-lrc
Review, vouch, and skip inside Claude Code — without leaving the chat surface where the code was generated.
- Natural language or slash commands (
/lrc:review) - Same LOC quota and review engine as git-lrc
- Bundled with git-lrc — no separate install needed
Install LiveReview directly in your IDE
Get instant AI code reviews without leaving your editor. Available for VSCode, Cursor, and Antigravity.
VSCode
Cursor
Antigravity
Quick Installation Guide
LiveReview Enterprise
Custom deployments, SSO integration, dedicated AI keys, and priority SLA support for scaling engineering organizations.
Security & Ops
Self-hosted deployment (optional), support for multiple organizations, custom domain hosting, SSO & User Directory integration (SAML/OIDC), and full data privacy.
Flexible AI & Models
Connect to private cloud LLMs or opt for fully self-hosted AI models using Ollama or your private infrastructure to guarantee no code leaves your network.
Custom Integrations
Custom API access, bespoke workflow integrations, and engineering insights dashboards tailored to your development tooling and platform requirements.
Dedicated SLA Support
Prioritized support channel with dedicated service level agreements (SLAs), custom development, and professional onboarding services.
AI code reviews without the vendor cloud
- No intermediary cloud — your code goes directly to the AI provider
- Your own API keys, your own infrastructure, Docker-based
- Typically 40–50% lower TCO vs cloud-only alternatives
Tailored options for internal networks, custom compliance, and high-volume workloads.
Security
Built for security review
LiveReview documents the answers enterprise teams ask first: deployment model differences, code/data handling, AI safeguards, supply-chain visibility, and clear security response timelines.
- Separate guidance for self-hosted/Ollama and cloud LLM deployments
- Explicit data handling: what leaves your network, when it happens, and retention/deletion expectations
- Prompt-injection and unsafe-output mitigations, automated scanners, SBOM visibility, and transparent GitHub source with responsive disclosure policy
Loved by the community
22 developers are sharing git-lrc on LinkedIn. Swipe through what they had to say.
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