Hupyy offers a pure‑logic computing platform that replaces probabilistic AI inference with formally verified proofs generated by SAT/SMT solvers. The platform provides a RESTful API and language‑agnostic SDKs for embedding deterministic, auditable decision outputs into enterprise workflows, available as cloud‑native or on‑premise deployments for regulated industries.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises in regulated domains rely on AI predictions that are inherently probabilistic, making it difficult to demonstrate compliance, audit decisions, or guarantee repeatable outcomes. This uncertainty hampers adoption of machine‑learning insights where legal or safety standards demand provable correctness.
Solution
Hupyy delivers a pure‑logic computing platform that substitutes probabilistic inference with formally verified proofs. The engine applies automated reasoning techniques—such as SAT/SMT solving and type‑checked proof construction—to generate a machine‑checkable artifact for every result. Because each answer is accompanied by a verifiable proof, organizations can audit, reproduce, and certify decisions without manual reinterpretation. The platform is exposed via a programmable API and SDK, enabling seamless integration into existing data pipelines and decision‑support systems. Deployments are offered as cloud‑native services or on‑premise packages, allowing regulated firms to meet data‑sovereignty and security requirements while retaining deterministic performance.
Target Audience
Primary customers are regulated enterprises—financial services, healthcare, aerospace, and government agencies—that require auditable, reproducible AI decisions, as well as AI platform providers seeking to add provable inference capabilities for their clients.
Features
- Formal verification core built on state‑of‑the‑art SAT/SMT solvers that produce mathematically sound proof objects for every inference
- Deterministic execution guarantees identical outputs for identical inputs across runs and environments
- RESTful API and language‑agnostic SDKs (Python, Java, Go) for embedding proof‑enabled inference into enterprise workflows
- Built‑in audit trail with versioned proof storage, supporting compliance frameworks such as ISO 27001 and FIPS 140‑2
- Scalable cloud‑native deployment model with isolated sandbox containers for secure multi‑tenant usage
- Open‑source reference implementation (GitHub) for transparency and extensibility
- Compatibility layer for exporting proofs in standard formats (e.g., JSON‑LD, ZK‑SNARK compatible statements)
- Role‑based access control and end‑to‑end encryption for proof data in transit and at rest