Reviewed against the ask

Every PR is checked against the ticket's business requirement, not only whether the code is clean. No coding agent does this by default; GReview gets the intent from Jira through GKS.

Through GKS

Inter-PR collision detection

Flags every other open PR touching the same functions, a conflict class point tools structurally can't see.

Unique to Garth

Your context, not the model's guess

Whole codebase, architecture rules, team guardrails, and scale and compliance guidelines, all served from GKS. One prioritized review, critical to low. The 4.2% false positive rate is context, not luck.

The brain matters

Maker-checker by design

The agent that wrote the code never signs it off. GReview reads only what the job needs through GKS and writes back only through your keys. Cloud, VPC, or on-prem.

Independent by design

A real review

This is what lands on the PR.

Not a wall of style nits. One assessment, findings ordered by what will actually hurt you in production, with the fix, the file, and the line.

Severity-first, so reviewers know where to look.

GReview opens with a verdict, then groups findings by impact. A hardcoded secret and a breaking API change surface first. Duplicate formatting logic sits at the bottom, where it belongs. Your reviewers read top-down and stop when the risk runs out.

Security that matters

Hardcoded live secrets, SQL injection, unsafe input handling, caught before merge, not in an incident report.

Breaking-change detection

New required parameters whose callers weren't updated: the runtime TypeError your tests would've found in prod.

Logic, performance & quality

N+1 patterns, edge cases, and duplicated logic, each with a concrete, apply-ready fix.

Every review can surface

Security vulnerabilities Breaking API & integration Performance & N+1 Runtime & logic errors Inter-PR conflicts Performance profile Code quality & duplication

85%

of reviewer work handled on the first pass. GReview speeds your reviewers up. It does not replace them.

Garth PR Agentcheckout & bulk-billing · reviewed just now

Do not merge

Overall Assessment

The goals are legitimate, but the implementation is critically flawed and should not be merged in its current state. It carries multiple severe security vulnerabilities plus breaking API changes that will cause runtime errors in production.

🔴 CRITICALSecuritycheckout.py:8

Hardcoded live Stripe secret key

A live secret key is hardcoded as a module-level constant and leaked further via a print statement, an immediate security-incident risk. Revoke and rotate the key, then move it to a secrets manager.

🔴 CRITICALSecuritybilling.py:24

SQL injection in process_bulk_billing

User IDs are interpolated directly into the query string. Use parameterized queries or an ORM to remediate.

🟠 HIGHIntegrationnotification.py:5

Breaking signature change to send_alert()

A new mandatory channel parameter is not reflected in its existing caller in billing.py, guaranteeing a TypeError at runtime. Update all call sites and add a regression test.

🔵 PERFPerformancebilling.py:22

N+1 query pattern at scale

A blocking loop issues one query per user (~5.0s for 1,000 users). A batched IN clause resolves it in a single round trip.

2 low-severity notes · code quality & exception handling▸ expand

6 findings across 4 files · conflicts with 2 open PRs · performance profile attached

Nobody else does this

It sees the PRs your reviewers can't hold in their head.

A reviewer looks at one diff at a time. GReview knows every open PR that touches the same functions, before two of them merge and quietly break each other.

Conflicts with Other PRsOpen PRs modifying functions also modified by this PR. Manual verification recommended.

Watching 2 open PRs

PR #1 by @SaphireFire feature/payment-logging

process_payment · payment.py process_refund · payment.py

PR #2 by @SaphireFire demo-pr

process_payment · payment.py process_refund · payment.py process_bulk_billing · billing.py generate_invoice · billing.py process_checkout · checkout.py format_price · checkout.py send_alert · notification.py

Reviewed against intent

It checks the code against what the ticket asked for.

Clean code that builds the wrong thing still ships the wrong thing. GReview reads the ticket's business requirement through GKS and reconciles every PR against it: what was asked, what was built, and what rode along that nobody asked for.

PAY-2187

Retry failed card payments with exponential backoffAcceptance criteria read from Jira · 3 expectations extracted

PR #412 · 6 files

MATCHED

Retry up to 3 times with exponential backoffImplemented in payments/retry.py and covered by test_retry_backoff.

retry.py:14

GAP

Customer notified after the final failed attemptNo call site reaches the notification service. The ticket's third criterion has no code behind it.

not found

BEYOND

Refund path changed in the same PRprocess_refund was modified, but no ticket asked for it. Flagged for the reviewer, not blocked.

payment.py:88

Verdict: 2 of 3 criteria delivered, 1 gap, 1 change beyond the ask. Every line links to the ticket or the PR, so the reconciliation is auditable.

No coding agent does this by default

Claude Code, Codex, or Cursor only know the ticket if someone wires up MCP and keeps a skill current. GReview gets the intent from Jira through GKS on every review, with nothing for your developers to set up.

Maker-checker, by design

The agent that wrote the code is never the agent that signs it off. The check runs on your organisation's context, not the author's session, so it does not share the author's blind spots.

The same reconciliation, at release

GRelease runs Ask vs Built across a whole release train from the same GKS context, so "did we build what we promised" has one answer at the PR and at the freeze.

Beyond a linter

It reasons about how the code will actually run.

GReview profiles the change, ranks the real hot paths, and hands back a fix with the numbers to justify it, the kind of insight a static reviewer never surfaces.

Top optimization targets

1

DatabaseConnection.executeapp/database.py · sequential in a loop

~98%

2

process_bulk_billingapp/services/billing.py · distinct query per user

~99%

3

simulate_workloadmain.py · invokes the unoptimized service

100%

Before · N+1 pattern

After · batched query

# 1 query per user
for user_id in user_ids:
  status = db.execute(
    f"...WHERE user_id = {user_id}"
  )

# single round trip
ids = ",".join(map(str, user_ids))
statuses = db.execute(
f"...WHERE user_id IN ({ids})"
)

Execution profile · 1,000 users1,000 queries · ~5.0s

Execution profile · 1,000 users1 query · ~0.005s

1,000×

faster on the hot path, measured, not estimated

The economics of review

Catch the regression in review, not in production.

A defect that reaches production costs many times what it costs to catch in review. GReview reads the whole codebase on the first pass, against your guardrails, so far more is caught before it ships.

Cost to fix the same defectIllustrative · based on published defect-escape multiples

Caught in review

baseline

Caught in production

many times more

Measured across GReview deployments

0%

Review time reduced

3.1×

Regressions caught pre-merge

0%

Review comments acted on

GReview speeds your reviewers up. It does not replace them.

How it works

Every review runs through the governed boundary.

GReview never touches your systems directly. It reads through GKS, the governed knowledge layer you control, and posts back only through your keys.

1

Asks GKS

On a new PR, GReview requests the context it needs from GKS: your repository graph, architecture rules, guardrails, and the ticket behind the change.

2

Policy & guardrail check

GKS checks policy and guardrails at intake, releasing only what this job is authorized to see.

3

Pulls only the diff it needs

Scoped context: the diff, the relevant symbols, your coding standards, the ticket's intent, and open PRs, nothing beyond it.

4

Posts a scoped review

A prioritized, actionable review lands on the PR, written back to your environment through your key.

Configured in Garth Universe

Your repos, your rules. Set once, enforced on every PR.

Garth Universe is the control plane. Choose which repos GReview watches, author your own guardrails and architecture rules, decide when it runs, and route it through your own model keys.

universe.heygarth.ai / agents / pr-review

PR Review Agent

Active · monitoring your connected repositories

Auto ReviewGarth automatically reviews each PR the moment it's raised. Turn off to trigger manually with garth-rr in a comment.

Guardrails & architecture rules6 active

BLOCK

No secrets or credentials committed to sourceFails review on hardcoded keys, tokens, and connection strings

BLOCK

Database access must go through the repository layerEnforces your architecture boundary, no raw queries in services

WARN

Public API changes require a changelog entryFlags breaking signature changes with no migration note

Author a rule

Files to exclude

*.test.jsdocs/**node_modules/***.generated.ts

Destination branches to exclude

developstaging

Repositories14 connected · 11 active

payments-servicePythonenabled

web-checkoutTypeScriptenabled

infra-terraformHCLexcluded

Model & context routing

Anthropic · connectedOpenAI · connectedJira · business context

~5 min setup Connect a repo and GReview is reviewing PRs out of the box. Every configuration after that is a click away in Universe.

Data Governance

Your data stays yours.

Agents never touch your systems directly. Everything reads through GKS, the governed boundary you control, and every action writes back only through your keys. Your own coding agents can read the same context over MCP. Sign-in is Google or Microsoft SSO, with roles and audit logs in Garth Universe.

Explore GKS →

01

You choose what connects

GKS ingests only the sources you authorize. Nothing you have not connected ever enters.

02

Only what the job needs

Each agent gets the exact data required to do its work, filtered at intake, and nothing beyond it.

03

Guardrails at the boundary

Agents read from GKS, never your host systems. Misuse stops at the boundary, not inside your stack.

04

Every action needs your keys

When an agent writes back to a system, it goes through credentials you provision, scope, and revoke at will.

GKS runs

Garth Cloud

In your VPC

On-prem

GKS runs in Garth Cloud. Your systems stream in over an encrypted, governed channel. Fastest to start, nothing to host.

Where GReview stands apart

Everyone owns a slice. Garth owns the layer.

Point tools each read one signal. GReview grounds review on the same shared brain as security, release, and ROI, so it sees what none of them can.

Capability	GReview	CodeRabbit	Greptile	Copilot
Whole-codebase context, not just the diff	✓	~	~	–
Inter-PR collision detection	✓	–	–	–
Team guardrail enforcement	✓	~	–	–
Ask vs Built verdict carried from the PR to the release train	✓	–	–	–
Governed org context served to your coding agents over MCP	✓	–	✓	–
Runtime performance profiling	✓	–	–	–
Governed data boundary · VPC / on-prem	✓	~	~	–
One shared brain across review, security & release	✓	–	–	–

✓ Full~ Partial– None

The wedge is competitive. The platform is not.

The number leaders can defend

Roughly $150K of value per 10-engineer team.

Reclaimed review time, faster merges, and fewer escaped defects. Move the sliders to your team and see the return.

Your team

Active developers25

Fully-loaded cost / dev / month$12,000

PRs per month (team)200

Hands-on review time / PR45 min

Avg PR wait time18 h

Your return with GReview

$202K

estimated annual value for your team

Current monthly review cost$19,350

GReview cost (25 devs × $18)$450

Net monthly savings$16,800

Return on GReview spend38× / $1

Illustrative model. Assumes GReview handles ~85% of first-pass review work and cuts PR wait time ~95%, with a bounded per-PR context-switch cost. PR-review impact only. Real value is typically higher across the wider workflow.

Pricing

Priced per developer, billed on who's active.

GReview bills on active developers, a contributor active in the trailing 30 days. Dormant seats are never charged.

Most teams start here

Standard

$18/ active dev / mo

Billed on active developers · annual billing available

Whole-codebase review, grounded on GKS
Inter-PR collision detection
Auto-fix workflow
Review & repo analytics
GitHub, GitLab, Bitbucket & Azure DevOps · Slack

Start a pilot

Enterprise

$24–40/ active dev / mo

Everything in Standard, plus:

SSO / SAML & audit logs
Custom guardrails & rules
Private model routing · BYOK
VPC & on-prem deployment
Priority support with SLA

Contact sales

Design-partner program

Building with the first cohort? Design-partner rates from $10–$12 / dev, plus a 6-week all-features trial with your own key. Bring your own model key and the price drops about a third.

Every plan includes GKS (the knowledge backbone), GAssist, and G-IDE.

Who it's for

Built for teams shipping faster than they can review.

Fast-growing startups

Consistent quality across a fast-changing codebase
Faster onboarding for new developers
Senior time back for architecture, not nits

Distributed teams

Immediate feedback regardless of time zone
Eliminated 24-hour review wait cycles
Fewer revision loops after human review

Enterprise orgs

Standardized code quality across every team
30–45% fewer security issues
Guardrails enforced centrally, on every PR

Security-sensitive orgs

Governed data boundary through GKS
VPC or fully on-prem deployment
Write-back only through your own keys

In Slack & Teams

Review that comes to you.

GAssist puts GReview in Slack and Teams. Review results, guardrail flags, and inter-PR collisions land in your channel, and you approve, request changes, or merge right there, without a tab switch.

Meet GAssist →

MD

Marco Diaz

@Garth what did GReview flag on #482?

GAssistAPP

GReview checked #482 against your full graph and guardrails:

2 guardrail hits · 1 inter-PR collision with #479 on retryConsumer()

ApproveRequest changesMerge

Govern your
AI code.