Every output verified. Every rule enforced. Mathematically proven.
LLMs in production are generating millions of outputs daily: customer responses, financial decisions, medical guidance. Traditional guardrails fail: regex misses semantics, prompt engineering fails silently, and LLM-based validators hallucinate.
Aare Verify uses automated reasoning to mathematically prove every LLM output satisfies your rules before it reaches users.
Managed Cloud Service
Real-time LLM output verification via REST API. Deploy in minutes with zero infrastructure. Scales automatically with your traffic.
Self-Hosted Cloud or On-Premises
Full control over your verification infrastructure. Deploy in your VPC, private cloud, or data center. Same API, your environment.
Airgapped or Mobile
On-device LLM verification for environments without connectivity. Native SDKs for iOS, Android, and embedded systems.
Aare Verify is powered by Z3, the same SMT solver that AWS uses to verify IAM policies, Microsoft uses to verify network configurations, and NASA uses to verify flight-critical software.
Every verification produces a formal proof certificate. Not probability, not best-effort - mathematical certainty that rules are satisfied or violated.
Verification runs after LLM generation, outside the prompt context. Jailbreaks and prompt manipulation cannot bypass enforcement.
Unlike regex and pattern matching, automated reasoning understands semantics. "35% DTI" and "debt-to-income ratio of thirty-five percent" verify identically.
Complex policies with interacting constraints are handled natively. Z3 was built for exactly this problem at AWS, Microsoft, and NASA scale.
Every decision includes a proof trace identifying exactly which rules passed or failed. Show regulators precisely why a response was blocked or approved.
Works with any LLM: GPT-4, Claude, Llama, Gemini, or your fine-tuned models. Verification logic is independent of the generation source.
|
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 (system prompts, "do not say" instructions) |
Automated Reasoning (post-generation formal verification) |
|
|---|---|---|
| Prompt injection / jailbreaks | Easily bypassed | Impossible - runs after LLM, outside prompt |
| Enforcement mechanism | Just hopes the LLM obeys | Hard enforcement that blocks non-compliant output |
| Mathematical guarantees | None | Formal proof of compliance for every response |
| Audit trail | None or vague | Certificate proving exactly which rule was/wasn't violated |
| Consistency across models | Varies wildly | 100% consistent - logic doesn't care about sampling |
| Complex policies | Breaks down quickly | Handles thousands of interacting rules natively |
| Bottom line | Best-effort, fragile | Mathematically guaranteed, future-proof |
GitHub: https://github.com/aare-ai