From Review Bottlenecks to Continuous Quality Gating
Reviewers focus on architecture, not mechanics.
Award-Winning AI Development & Consulting
IT / Engineering
Senior Engineers Spend 4 Hours a Day Reviewing Formatting — Not Architecture
48% of developers say code review is their biggest bottleneck — senior engineers spending 2–4 hours daily on naming conventions a rule engine catches in seconds (SmartBear).
Read the full evidence
Fixing defects at review costs 10x less than in production; security flaws caught late cost 100x more.
100x The pre-screener strips the mechanical issues consuming 60–80% of review time, making every hour 4–5x more productive.
Business impact
100x
more expensive to fix a defect in production than in code review — the economic case for pre-screening every commit
Source: IBM
- 100x
- 76% of apps carry vulnerabilities at first production scan — average fix time 205 days (Veracode).
Core Capabilities of the AI Code Review Pre-screener
SAST, complexity, coverage, and PR comments.
Code Quality
Code Quality & Standards Analysis
Complexity scoring and code smell detection.
SAST Pre-screening
SAST Vulnerability Pre-screening
OWASP Top 10 and CWE Top 25 SAST per diff.
Test Coverage
Test Coverage Gap Detection & Quality
Untested branches flagged before merge.
Performance Detection
Performance Anti-pattern Detection
N+1 queries and O(n²) hot paths flagged.
Review Comments
First-Pass Review Comments
Inline annotations with risk and fix.
Quality Gates
CI/CD Integration & Quality Gates
Quality gates block merges past thresholds.
Why Choose Bonami's AI Code Review Pre-screener
Code review eats 2–4 senior-engineer hours daily. We automate the mechanical 80%.
From Style Comments to Architecture-Level Code Review
Style comments gone — every comment is substantive.
From Security Afterthoughts to Shift-Left Vulnerability Prevention
Security findings land inline, while code is fresh.
Integrations
Works With Your Existing Engineering & DevSecOps Stack
The pre-screener connects to your source control, CI/CD pipelines, issue trackers, and static analysis tools.
7 named systems it connects to
Running something not listed here? Tell us on the call — scoping the connector is the first step.
AI Code Review Pre-Screener
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GitHub
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GitLab
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Bitbucket
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Azure DevOps
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Jira
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SonarQube
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Slack
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AI Code Review Pre-Screener — FAQs
[ 1 ]
What is AI code review and what does this agent analyse?
Every PR is scanned for quality, security, coverage, and performance gaps.
[ 2 ]
Which programming languages and frameworks does the pre-screener support?
Java, Python, JS/TS, Go, C#, Ruby, PHP, Rust, plus Terraform and Kubernetes.
[ 3 ]
How does the SAST security scanning work and how accurate is it?
AST and taint flow analysis, with false positives typically under 5%.
[ 4 ]
Does the pre-screener replace SonarQube or other existing static analysis tools?
It complements SonarQube, Snyk, and Semgrep — findings arrive in the PR.
[ 5 ]
How are quality gates configured and what happens when a PR fails?
Gates block merge on CVSS 7.0+, credentials, or coverage regressions.
[ 6 ]
How does the pre-screener handle large PRs and monorepo architectures?
Diff-only analysis runs under 60 seconds; monorepos scope to changed modules.
[ 7 ]
How does the AI-generated review summary help human reviewers?
It summarises the change, ranks findings by severity, and rates overall risk.
[ 8 ]
What does implementation look like and how quickly can it run on our repositories?
Live in 1–2 weeks: integrate, baseline scan, then calibrate quality gates.
[ 9 ]
How does this automated code review tool deliver pull request review automation?
It reviews every PR the moment it opens — cutting cycle time by 55%.
[ 10 ]
How is this AI code review software different from a linter or basic code review automation?
Beyond linting: SAST, taint flow, complexity, coverage, and CI/CD gates.
Fix It in Review for 1x — or in Production for 100x.
Every hour spent on style comments is an hour not spent on architecture and security logic. See the pre-screener running against a real repository — a pre-screened PR with automated security, quality, coverage, and performance findings, plus the AI-generated summary that arrives before any human reviewer opens it.
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