aare.ai_

Enterprise AI Compliance.

Every output verified. Every rule enforced. Mathematically proven.

Today, autonomous AI agents are addressing customer inquiries, aiding in claim processing, drafting contracts, closing sales, and making critical business decisions, often without human involvement.

A single non-compliant response can trigger regulatory fines, lawsuits, or irreversible reputational damage.

aare.ai verifies the validity of LLM output in real time. LLM responses are validated against your exact compliance policies before anything reaches your customers.

The aare.ai /verify API is powered by Automated Reasoning with Z3 https://www.microsoft.com/en-us/research/project/z3-3/, a theorem prover trusted by NASA, Amazon, and Microsoft. aare.ai transforms your enterprise and regulatory rules into precise, executable formal logic for unbreakable enforcement.

Every response is mathematically proven compliant. If it passes, it's delivered. If it fails, it's blocked, with an auditable proof certificate that pinpoints the exact rule and clause violated.

aare.ai /verify API

1. YOUR APP, BOT, OR AGENT
LLM Request
→
2. LLM
Response
→
3. AARE.AI
Automated Reasoning
→
4. RESULT
true or false
Compliant Response (Passes Verification)
Request to aare.ai API: 
POST /verify
{
  "llm_output": "Loan approved for $75,000
    at 6.5% APR. Credit score: 720,
    DTI: 35%, down payment: 10%.",
  "ontology": "fair-lending-v1"
}
Response from aare.ai API: 
{
  "verified": true,
  "violations": [],
  "parsed_data": {
    "amount": 75000, "credit_score": 720,
    "dti": 35, "down_payment": 10
  },
  "proof": {
    "verification_id": "a3d8c1f2-5b7e-4a9d-8c6f-1e2b3a4d5c6e",
    "solver": "Z3 SMT Solver",
    "timestamp": "2025-12-01T00:18:42.156839"
  },
  "execution_time_ms": 12
}
Non-Compliant Response (Blocked with Violations)
Request to aare.ai API: 
POST /verify
{
  "llm_output": "Loan approved for $150,000.
    Credit score: 580, DTI: 48%,
    down payment: 3%.",
  "ontology": "fair-lending-v1"
}
Response from aare.ai API: 
{
  "verified": false,
  "violations": [
    {"rule": "LOAN_AMOUNT_LIMIT",
     "actual": 150000, "expected": "≤ 100000"},
    {"rule": "MIN_CREDIT_SCORE",
     "actual": 580, "expected": "≥ 600"},
    {"rule": "MAX_DTI",
     "actual": 48, "expected": "≤ 43"},
    {"rule": "DOWN_PAYMENT",
     "actual": 3, "expected": "≥ 5"}
  ],
  "proof": {
    "verification_id": "7e2f1a3b-9c84-4d6e-b5a1-8f3c2d9e0b7a",
    "solver": "Z3 SMT Solver",
    "timestamp": "2025-12-01T00:22:17.839216"
  },
  "execution_time_ms": 14
}

Try It: Verify LLM Output in Real Time (live demo)

Applied Rule Set:
Loan ≤ $100k
Credit ≥ 600
DTI ≤ 43%
Down Payment ≥ 5%
Choose an LLM response:
Click "Verify" to see results
Parse → Extract → Verify → Prove

Pattern Matching vs. Automated Reasoning

Pattern Matching
(Regex, keyword lists, etc.) 		Automated Reasoning
(Z3-powered formal logic)
Rephrasing Breaks instantly Works with any phrasing
Math & calculations Cannot compute relationships Full mathematical reasoning
Complex rule interaction No understanding of interactions Fully compositional logic
Proof of compliance None Generates formal proof certificates
Maintenance at scale Hundreds/thousands of brittle rules Scales cleanly to 10,000+ rules
Bottom line Fragile, high false positives/negatives Mathematically guaranteed correctness

Prompt Guardrails vs. Automated Reasoning

Prompt Guardrails
(system prompts, "do not say" instructions, etc.) 		Automated Reasoning
(post-generation formal verification with Z3)
Prompt injection / jailbreaks Easily bypassed Impossible as it runs after the LLM, outside the prompt
Enforcement mechanism Just hopes the LLM obeys Hard enforcement that blocks non-compliant output
Mathematical guarantees None Formal proof of compliance for every single response
Audit trail None or vague Certificate proving exactly which rule was/wasn't violated
Consistency across models & temperatures Varies wildly 100% consistent logic doesn't care about sampling
Complex & compositional policies Breaks down quickly Handles thousands of interacting rules natively
Bottom line Best-effort, fragile Mathematically guaranteed, future-proof

Explore More Demos

High-Stakes Verification Demo

Explore how automated reasoning verifies critical AI decisions in high-stakes scenarios like trading, medical recommendations, loans, and contracts. Select different LLMs and see formal verification in action.

Launch Demo

Guardrail Enforcement Demo

Catch LLM violations before they reach production. Test scenarios in customer service, content moderation, data privacy, and financial advice to see how guardrails are enforced and violations detected.

Launch Demo

Mortgage Compliance Demo

Verify mortgage-related LLM outputs against 188 regulatory constraints covering TILA, RESPA, ECOA, and fair lending requirements. See how complex multi-rule verification works in real time.

Launch Demo

HIPAA Compliance Demo

Ensure healthcare AI outputs meet HIPAA requirements. Verify PHI handling, minimum necessary standards, authorization requirements, and audit trail compliance with 52 formal constraints.

Launch Demo

Get Started with aare.ai

Self-Host aare.ai

Run aare.ai on your own infrastructure. Open-source, fully customizable, and production-ready.

Pull the code from GitHub, deploy in minutes, and own your verification stack.

Get the Code on GitHub

Managed Hosting

Let us host and operate aare.ai for you on AWS or Azure.

Enterprise-grade SLA, private VPC, audit logs, SOC 2 readiness, and dedicated support.

Contact Sales

Consulting & Integration

Expert help building LLM verification into your products and workflows.

Ontology design, compliance mapping, custom rule authoring, and production integration.

Book a Free Consult

GitHub: https://github.com/aare-ai