Complementary tools: contract-first generation + runtime validation (Correctover)

[Correctover]

Complementary Tools: Contract-First + Runtime Validation

Hi! 👋

Contrix is doing something really interesting — contract-first structured output for local LLMs. We've built a complementary tool called Correctover (https://correctover.com) that operates at the runtime validation layer.

The overlap & the gap

Contrix ensures the LLM produces structured output matching a contract at generation time.
Correctover validates LLM outputs after generation across 6 dimensions (schema, semantic equivalence, latency, observability, policy, custom) and self-heals failures.

Together they'd cover the full lifecycle:

1. Contract definition (Contrix) → Define what good output looks like
2. Structured generation (Contrix) → Guide the LLM to produce correct output
3. Runtime validation (Correctover) → Verify the output actually meets the contract
4. Self-healing (Correctover) → Auto-recover when validation fails

Correctover at a glance

• 6-dimension CANON validation (22μs P50 latency)
• 3-level self-healing: L1 retry → L2 degradation → L3 failover (97.4% recovery rate)
• Drift detection with flywheel learning (88 rules from 20K+ calls)
• Circuit breaker + MCP Server support
• BYOK native — zero proxy overhead

Integration idea

Contrix could expose a validation hook where Correctover's contract checks run after generation:

// Contrix generates structured output
const result = await contrix.generate(prompt, schema);

// Correctover validates + self-heals if needed  
const verified = await correctover.validate(result, {
  contract: schema,
  provider: 'ollama/llama3',
  selfHeal: true
});

Benchmark data

We ran 20,071 calls across 9+ providers. Key findings:

• Schema validation alone catches ~40% of failures
• Adding semantic equivalence catches another ~30%
• Self-healing recovers 97.4% of recoverable failures
• Total validation overhead: ~22μs (negligible)

Would love to explore how these tools could work together. Happy to provide a PoC or share our benchmark methodology.

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PyPI: https://pypi.org/project/correctover/
Website: https://correctover.com