Panto AI is an AI-powered code review agent designed to integrate seamlessly with your GitHub, GitLab, Bitbucket, and Azure DevOps workflows. Its proprietary technology aligns code with business context, automatically fetching information from Jira and Confluence to review PRs more intelligently. Panto AI focuses on enhancing code quality, ensuring security compliance, and reducing cognitive load on developers by offering meaningful, context-driven reviews.
Performance Score
A+
Content/Output
Contextual, High-Precision Reviews
Interface
Clean, Developer-Friendly UI
AI Technology
- Reinforcement Learning
- Business Context Mapping
- NLP for PR Summaries
- Secure Code Scanning
Purpose of Tool
Automate context-driven code reviews to boost security and quality at scale
Compatibility
Web-Based; GitHub, GitLab, Bitbucket, Azure DevOps Integrations
Pricing
Free Trial Available + Paid Plans
Who is Best for Using Panto AI?
- Mid to Large Dev Teams: Scale code reviews without sacrificing quality.
- Tech Startups: Move faster with fewer bugs reaching production.
- Enterprise Engineering Managers: Standardize code quality across teams.
- DevOps & SRE Teams: Ensure consistent security and code health checks.
Auto-fetch Business Context from Jira & Confluence
30,000+ Security Checks Across 30+ Languages
High Signal-to-Noise Code Reviews
Reinforcement Learning to Improve Accuracy
Customized Code Review Reports
On-Premise Deployment Available
Zero Code Retention Policy
CERT-IN Compliance Certified
Is Panto AI Free?
Panto AI offers a free trial to help teams experience the benefits firsthand before committing to a subscription plan. Pricing details are customized based on team size and needs, and must be requested via the sales team.
Panto AI Pros & Cons
Seamlessly integrates with GitHub, GitLab, Bitbucket, Azure.
Context-aware reviews cut down meaningless feedback.
Supports 30+ coding languages with security checks.
On-premise deployment available for data-sensitive companies.
Zero code retention for maximum privacy compliance.
Pricing not publicly listed.
Setup may require configuration effort for business context.
Might be overkill for very small dev teams.
Limited brand recognition compared to older DevOps tools.
