Make AI-Generated Code
Secure & Maintainable
AI agents write code fast. RETER makes sure it's secure and clean — catching vulnerabilities, spaghetti architecture, god classes, and hidden duplication before they ship.
The Bottleneck
Has Moved
Writing code used to be the bottleneck. AI solved that. Now the bottleneck is understanding — knowing what already exists, how it fits together, where the security boundaries are, and what will break if you change it. Agents that can't see the codebase as a whole produce code that's fast to write and expensive to maintain.
AI Agents Ship
Vulnerable Spaghetti
They Introduce Vulnerabilities
Command injection, SSRF, credential leaks, request smuggling — AI agents produce code with security flaws they can't see.
They Cross Trust Boundaries
Mixing untrusted input with privileged operations, passing user data straight to shells and databases.
They Create God Classes
Dumping everything into whatever file is open. 2,000-line classes with 40 methods that handle auth, logging, parsing, and business logic all at once.
They Duplicate Instead of Reuse
Writing new auth handlers, HTTP parsers, and validators instead of using the hardened ones that already exist three directories away.
They Ignore Architecture
No awareness of layers, boundaries, or patterns. Calling infrastructure from presentation. Mixing concerns freely. The result is untraceable spaghetti.
They Reinvent Patterns
Every agent session starts from scratch. Established design patterns, naming conventions, and project structure are invisible to them.
They Skip Authorization
API endpoints without ownership checks. gRPC handlers without caller validation. Sandbox operations without permission gates.
They Accumulate Debt
Each session adds more code, more duplication, more coupling. Without structural awareness, every change makes the next one harder.
Symbolic Intelligence +
Security Analysis
Security Audit
Automated detection of command injection, SSRF, credential exposure, request smuggling, and OWASP Top 10 vulnerabilities — with CWE classification and proof-of-concept generation.
Code Ontology
A formal semantic model of your entire codebase — trust boundaries, dependency chains, class hierarchies, layer boundaries — built with symbolic reasoning.
RAG & ML
Semantic similarity search finds existing implementations before the agent writes a new one. Clustering detects hidden duplication across the entire codebase.
Spaghetti Untangler
God class detection, feature envy analysis, shotgun surgery tracking, long method extraction — 130 pipelines that find the mess and tell the agent exactly how to clean it up.
Architecture Guard
Enforces layer boundaries, detects circular dependencies, and prevents cross-concern coupling. The agent sees the architecture before it writes a single line.
Dead Code & Drift
Finds uncalled methods, orphaned classes, unused imports, and code that drifted from its original design intent. Less code, fewer attack surfaces.
Meta Prompting
Workflow prompts that orchestrate the AI agent through structured multi-phase analysis. Prompts and their linked analysis scripts co-evolve through a genetic algorithm — crossover, mutation, selection — getting sharper with every run.
Detect → Classify → Fix
Detect
130 pipelines scan the entire codebase: injection surfaces, credential flaws, god classes, dead code, circular dependencies, duplicated logic, missing authorization.
Classify
The AI agent reads actual code, triages each finding — CWE classification for vulnerabilities, severity ranking for code smells — and separates true positives from noise.
Fix
The ontology guides every fix: where to add input validation, which class to extract, which method to inline, which trust boundary to enforce. The agent refactors with full architectural context.
Get in Touch
Secure code. Clean architecture.
No more spaghetti.
Give your AI agent the visibility it's missing.