Build with Baz

If you are weighing whether to build or buy AI review agents, Baz gives you the best of both worlds.

The first review is easy. The next thousand are the real test.

A model, a diff, and a prompt can get you a review. Scaling it takes consistent rules across agents, learning from developer feedback, visibility into every review, and models that balance quality and cost.

If you’re considering buying a reviewer, you don’t have to start all over. Baz builds on your existing standards, knowledge, and instructions with five layers of intelligence that keep reviews grounded in your expertise as you scale.

The scaling gapThe bottleneck has moved from writing code to trusting it.
Agentic coding outputHuman review capacityThe gapBugs, rework, incidents

Incidents per PR rose +23.5% year over year as AI coding went mainstream (Cortex, 2026).

Your engineering expertise, built into every review

Your tools. Three phases. One shared review layer.

Your code (and tooling)

Build

  • Jira
  • Figma
  • Linear
  • Notion
  • Fibery
  • Monday
  • Shortcut
  • YouTrack
  • Claude
  • Cursor
  • Codex
  • Devin
  • Baz MCP
  • Baz CLI
Baz reviewers for build
  • Plan Review
  • Security Reviewer
  • Code Correctness
  • Code Quality
  • AI Coding Guidelines
  • Auto FixerIn your sandbox

Deploy

  • GitHub
  • GitLab
  • Azure DevOps
Baz reviewers for deploy
  • Verification Agent
  • Auto MergerIn your sandbox

Maintain

  • Datadog
  • Sentry
  • Coralogix
Baz reviewers for maintain
  • AI SRE Agent
  • Advanced Security Reviewer
  • Skills Maintainer
Baz review layer
InstructionsYours + BazSkills, rules, harnesses
Context-awarenessConventions & Guidelines
GovernanceReporting & Metrics
Reinforced learningMemory
InferenceYours + BazCommercial and proprietary

Instructions

Baz combines your skills, rules, and harnesses with its own and keeps review instructions current as your codebase evolves.

Baz reviewers also work with Claude, Codex, Cursor, and Devin through our MCP and plugins, steering the code while it's written, rather than after the PR opens.

Context-awareness

Baz brings together codebase knowledge, engineering standards, ownership, developer feedback, and product intent across repositories. Reviewers draw on this shared context to assess each change’s wider impact.

Reinforced learning

Baz coordinates reviews, prioritizes findings, and learns from developers’ everyday PR reviews and comment responses. That feedback automatically informs future reviews.

Inference

Use Baz’s out-of-the-box commercial models, your own, or our specialized open-weight review model. Baz pairs reviewers with models that balance review quality with cost. Our specialized model draws on real developer feedback and findings engineers act on to deliver proprietary review intelligence.

Governance

Baz’s reports and dashboards give engineering leaders a shared view of review quality, developer engagement, and impact across teams and repositories.

Self-host Baz in your own cloud to keep sensitive data within your environment.

Why teams choose to build with Baz

Spend less time sorting reviews

Move from feedback to resolution without starting over on every push. Baz keeps reviews focused as PRs evolve, so engineers spend less time sorting repetitive comments and more time preparing to merge.

Read the LSports story
LSportsAcross 241 repositories
26% lower P90 lead timeBefore Baz: 96.9 hoursWith Baz: 71.7 hours96.9h71.7h25.2h lessBefore BazWith Baz

P90 hours from change to merge: the slowest, most complex PRs.

A reviewer that learns from your team

Get feedback that becomes increasingly relevant to how your engineers work. Your team’s responses shape future reviews, so you can make reviews more useful without spinning up a separate workstream for prompt tuning.

Explore how Baz learns
AI Coding Guidelines
  • Do not suggest moving an integration to an endpoint the deployed token cannot call.

    2 hours ago
  • Do not suggest parallelizing writes that run once per organization.

    1 day ago
  • Do not ask for separate disabled and hidden state tests when one render test covers both.

    3 days ago

Consistent standards across every reviewer

Keep a consistent review bar as repositories and rule files multiply. Baz manages rule hierarchies and applies relevant standards, reducing the work of reconciling different agents’ interpretations of your guidelines.

Explore the Baz plugins

/baz:review

baz - remote_grep (MCP)(repository: "acme/api", pattern: "idempotencyKey")

3 matches in src/billing/

Should fixsrc/billing/retry.ts:42

The retry calls charge() without an idempotency key, so a timeout can bill twice.

Team rule: Idempotency, from AGENTS.md · acme/api

## Coverage: acme/payments, acme/api

Deeper understanding with the right scope

Catch business-logic issues that look correct in the diff but break assumptions elsewhere. Baz investigates related code within the review’s scope, without pulling engineers into unrelated files or another team’s code.

Explore pull request reviews
src/pages/prConversation/components/DeltaDiffView.tsx+12
Baz ReviewerHighOpen

This exits unreviewed mode when no discussions are open, but CommentsView still enables the toggle whenever unreviewedCount > 0, so the toggle can flip off on mount.

Visibility into review impact

Make rollout and tuning decisions using review performance across your organization. See where feedback helps and where teams need attention, without building a reporting system from scratch.

Explore Interactions
953 User interactions

Deploy on your terms

Choose the setup that fits your security requirements and the infrastructure you already operate.

baz.ai

Managed SaaS

Baz as a fully managed SaaS platform. Nothing to operate on your side.

storage: your cloud

Private Mode

An option on SaaS: repositories stay in your infrastructure while Baz runs the platform.

runtime: your cloud

Self-hosted

Self-host the platform and review runtime in your own cloud, under your own controls.

Get predictable economics

Your model bill is only the starting point.

What does review cost for 100 developers?

An illustrative year, starting with model usage.

Team size
100 developers
DIY model usage
$60 / dev / month
DIY annual model bill
$72,000
Baz standard code review
$30 / dev / month
Baz annual review cost
$36,000
Annual difference$36,000

Not counted above: the engineering work to build the pipeline, evaluate its output, and keep it reliable as models and workloads change.

Per developer, per month
Baz standard code review$30
DIY model usage only$60

Engineering time to build, operate, and maintain the DIY reviewer is additional.

With usage-based tooling, the bill climbs as your team and PR volume grows. Baz is priced per seat, so more PRs and revisions don't add per-run charges. That's a budget you can forecast instead of one you watch all month.

See Baz pricing

Illustrative scenario, not a measured customer average. Actual DIY costs vary by model, PR volume, context size, and review frequency.

When Baz isn’t the right fit (yet)

If you run a single coding agent with a small rule set and modest PR volume, a homegrown reviewer may be all you need. These open source tools from the Baz team are free to use in your own setup.

Awesome Reviewers

Give your reviewer a head start with instructions drawn from real code review discussions. Find standards relevant to your stack and load them directly into your agents, skills, or harnesses.

Explore Awesome Reviewers

Gion

Run more coding agents without weighing down your local machine. Gion fetches only the files each task needs, reducing local storage, token usage, and setup overhead. Best of all, Gion is Git compliant, so you can keep working the way you already are.

Explore Gion

See the difference

Put Baz to the test

Bring your internal reviewer and a set of representative PRs. See where Baz can strengthen your setup.