Semantic AI

Making AI-Generated Code Safe for Production

Mission

Semantic AI is building the future of secure software development by providing AI-powered code review tools that help engineering teams ship AI-generated code 3x faster without security regressions.

What We Build

Core Products

• Semantic AI Engine: AI-powered code security review platform
• GitHub Integration: Native PR analysis with inline comments
• Security Rules Engine: OWASP Top 10 + AI-specific vulnerability detection
• Production Readiness Scoring: Comprehensive code quality assessment

Key Features

• Security Vulnerability Detection: Advanced pattern matching beyond regex
• Production Readiness Scoring: 0-100 score with actionable recommendations
• GitHub Native Integration: Seamless PR workflow integration
• Semantic Analysis: Code understanding using AST and embeddings
• Actionable Guidance: Not just "bug found" but "here's the fix"

Technology Stack

Backend

• Language: Python 3.11+
• Framework: FastAPI
• Parser: Tree-sitter (multi-language AST)
• Analysis: Semgrep + semantic embeddings
• Database: PostgreSQL + Redis
• AI/LLM: Voyage-Code-3 embeddings

Frontend

• Framework: Next.js 14 (App Router)
• Language: TypeScript
• Styling: Tailwind CSS
• UI Components: shadcn/ui
• State Management: React Query

Infrastructure

• CI/CD: GitHub Actions
• Container: Docker + Kubernetes
• Monitoring: Prometheus + Grafana
• Cloud: Google Cloud Platform

Impact Metrics

Security Detection

• Accuracy: 90%+ detection of actual vulnerabilities
• False Positive Rate: <10% (vs. 20%+ for generic tools)
• Coverage: 95%+ of OWASP Top 10 patterns
• Performance: <30 seconds for 10K lines analysis

Business Metrics

• Target: 3x faster AI code deployment
• Goal: $100K+ ARR by Q2 2024
• Customers: 3+ paying customers by Week 2
• Repositories: 1000+ repos analyzed by Week 3

Problem We Solve

The Challenge

• AI Code Explosion: 40% of new code is AI-generated
• Security Gaps: Traditional tools miss AI-specific vulnerabilities
• False Positives: Generic tools have 20%+ false positive rates
• Slow Reviews: Manual security reviews take hours
• Production Risk: AI code deployed without proper security checks

Our Solution

• AI-Native Security: Built specifically for AI-generated code patterns
• Semantic Understanding: Goes beyond regex to understand code meaning
• Integrated Workflow: Native GitHub integration for seamless reviews
• Actionable Insights: Provides specific fixes, not just warnings
• Fast Analysis: Sub-minute analysis for typical PRs

Security Focus

Vulnerabilities We Detect

• OWASP Top 10: SQL injection, XSS, hardcoded secrets, etc.
• AI-Specific: Prompt injection, unsafe eval, model injection
• Custom Patterns: Extensible rule system for new threats
• False Positive Reduction: Advanced pattern matching

Security Architecture

• Multi-Layer Defense: Static analysis + semantic analysis
• Real-Time Detection: Continuous monitoring and alerts
• Compliance Ready: GDPR, SOC 2, and security standards
• Transparent: Open source core with enterprise features

Open Source Commitment

What's Open Source

• Core Engine: Semantic analysis and security rules
• GitHub Integration: Webhook handling and PR analysis
• API Framework: FastAPI application structure
• Documentation: Comprehensive guides and examples

What's Commercial

• Advanced Rules: Custom security rule development
• Enterprise Support: Priority support and SLA
• On-Premise: Self-hosted deployment options
• Analytics: Advanced reporting and analytics

Resources

Documentation

Community

Learning

Target Audience

Primary Users

• Engineering Teams: Using AI code generation tools
• Security Engineers: Need better AI code security tools
• DevOps Teams: Implementing security in CI/CD pipelines
• Startups: Fast-moving teams using AI for development

Getting Started

For Developers

1. Install: pip install semantic-ai
2. Configure: Set up GitHub App integration
3. Analyze: Start reviewing PRs automatically
4. Customize: Add custom security rules