The Problem

Enterprises now run LLM agents in production that answer customers, approve claims, draft contracts, and make decisions worth millions. These agents operate autonomously, often without a human in the loop.

Traditional defenses collapse at scale:

• Regex and keyword filters are trivial to bypass
• Prompt engineering fails silently
• Post-generation LLM "guard" models hallucinate or miss edge cases
• Human review is impossible at volume

Result: one non-compliant sentence can trigger regulatory fines, class-action lawsuits, or irreversible reputational damage.

The Solution

aare.ai eliminates compliance guesswork by treating every LLM output as a formal logic problem and proving it correct before it leaves your system.

Instead of hoping the model behaves, we mathematically verify that it did.

How It Works

1. Ingest any LLM output (free text, JSON, tables, bullet points)
2. Extract every factual claim with semantic accuracy
3. Apply your exact compliance ontology, encoded as first-order logic constraints in Z3
4. The theorem prover returns either:
   • a machine-verifiable proof of compliance, or
   • an immediate block + precise counterexample showing which rule and clause was violated

No sampling. No probabilities. Zero false negatives for the rules you encode.

aare.ai /verify

• Audit trails that actually withstand regulatory scrutiny (proof certificates, not screenshots)
• Works with any model, any prompt, any provider; no retraining required
• Scales to 10,000+ constraints without slowing down
• Production-grade latency that doesn't hurt response times
• Open-core architecture and Open Source: self-host or use our managed service on AWS, Azure, GCP, and Watsonx

Who Built aare.ai /verify

aare.ai emerged from years of hands-on engineering leadership in regulated sectors where a single overlooked failure can cascade into financial ruin or human harm.

Watching "good enough" LLM rollouts repeatedly backfire in these arenas led to a simple conviction: enterprise AI should never be probabilistic guesswork. It deserves the same ironclad reliability as humans and traditional software.

Built by Marc Kocher, a software systems builder who has led engineering teams at AWS and PayPal who decided to stop complaining and start building.

Ready to remove compliance risk from your LLM deployments?

Contact us