Infrastructure for
Autonomous Code Improvement

Makes AI-generated code enterprise-deployable. Deterministic, auditable, model-agnostic.

70%
Real-World CVEs
98%+
Benchmark Accuracy
vs Pattern Matching

Proven on the Hardest Test
of Our Infrastructure

Security verification is our first vertical — if Circle-IR can catch real-world vulnerabilities, it can verify any compliance requirement.

CWE-Bench-Java · 113 Real CVEs
70%
Detection Rate

Multi-file, cross-function vulnerabilities from production codebases.
The benchmark most tools score lowest on.

Industry Benchmarks

OWASP Benchmark
Perfect Score
2,740 cases · 0% FP
Juliet (NIST)
Perfect Score
NIST standard · 0% FP
SecuriBench Micro
98%+ Detection
Stanford · 7% FP

Spec → Generate → Verify
→ Refine → Ship

The missing layer between LLM generation and production deployment. Autonomous improvement until your code is enterprise-ready.

📝

Specifica

Write what "correct" means in plain language — not code. Specs become the ground truth and audit trail. specifica.org →

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Any LLM Agent

Model-agnostic. Works with Claude, GPT-4, Gemini, or any coding assistant. Generates code against your specifications.

Circle-IR Runtime

Extracts logic facts from generated code and verifies against specs. Deterministic, auditable, reproducible. If violations found, the agent autonomously refines until correct.

Currently supporting Java & JavaScript/TypeScript. Python, Rust in early stages of testing.

cognium — security scan

Make Your AI Code Production-Ready

Join the waitlist for early access to the infrastructure layer that makes autonomous coding work in regulated environments. Starting with security, expanding to full compliance (PCI-DSS, HIPAA, SOX).

No spam. We'll only email you when we're ready for beta testers.