Correctover — LLM Reliability Engineering

Your LLM works in staging. It fails in production. We fix that.

Correctover is the first LLM Reliability Engineering Platform — a complete engine for self-healing, semantic validation, and drift detection that ensures your LLM applications actually work in production.

Production-Proven Conformance Infrastructure

20,071 real API traces | 9 providers | 120,426 provider verdicts | 97.4% D-group recovery rate

Correctover's conformance runner has classified every production trace into a standardized fault taxonomy, directly driving the evolution of the PHI-OMEGA-RUNTIME Transition Sufficiency Conformance Contract v0.2.1.

Two contract refinements were adopted based on our production data:

• Fresh() predicate: Support must be current, not just present (stale_evidence_under_intact_schema)
• Fragment-level validation: Every support fragment must be transition-relevant (object-level support ≠ fragment-level transition support)

Full evidence: assets/evidence/

Why Correctover?

Traditional LLM tooling (gateways, proxies, observability) only routes requests or monitors metrics. They see the problem — but they don't fix it.

Correctover closes the loop:

	Traditional Gateways	Observability Tools	Correctover
See the problem	✅	✅	✅
Understand semantics		❌	✅
Fix it automatically	❌	❌	✅
Learn from failures	❌	❌	✅
Full reliability loop	❌	❌	✅

LiteLLM routes. Helicone watches. Portkey configures. Correctover ensures.

Three Core Engines

🔧 Self-Healing Engine

MAPE-K adaptive loop with 4-level recovery:

1. Retry — smart retry with exponential backoff
2. Degrade — downgrade to lighter model
3. Switch — failover to alternative provider
4. Repair — semantic context reconstruction

📐 Semantic Validation Engine

6-dimensional contract verification on every API interaction:

• Authorization integrity
• Context stability
• Execution fidelity
• Terminal outcome verification
• Policy freshness
• Authority validity

📊 Drift Detection Engine

Continuous monitoring of model behavior changes across:

• Response quality degradation
• Latency anomalies
• Cost pattern shifts
• Failure rate escalation

Ecosystem Compatibility

Verified integrations with major AI agent frameworks:

Framework	Adapter	Status
CrewAI	correctover-crewai	✅ Active
Ibex	correctover-ibex	✅ Active
Patronus	correctover-patronus	✅ Active

Community & Standards

Contributing to ecosystem stability across major AI agent frameworks:

Framework	Stars	Issue	Finding
CrewAI	52K+	#6380	Async LLM failures silently freeze flow execution
Smolagents	26K+	#2432	No timeout/retry — infinite hang on API overload
LlamaIndex	45K+	#22180	Official docs teach zero error handling patterns

All patches include reproduction steps and minimal pure-Python fixes. No dependencies required.

Conformance Benchmark

Portable conformance test suite: guardrail-conformance-benchmark

Production-derived evidence archive: assets/evidence/

SDK

Python SDK: correctover-sdk

Contact

• Website: https://correctover.com
• Email: wangguigui@correctover.com
• GitHub: @Correctover

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Keywords: Agent runtime conformance, PHI-OMEGA, AI agent governance, production fault dataset, LLM reliability, self-healing, semantic validation