This startup offers a privacy-preserving inference platform that allows AI models to run on encrypted data, ensuring user queries remain confidential. By utilizing fully homomorphic encryption, the platform enables AI providers to deploy models without accessing sensitive user data, protecting privacy from AI providers, cloud infrastructure, and intermediaries.
Funding
$3.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
AI model deployment often requires access to sensitive user data, creating privacy risks for end-users and compliance challenges for AI providers. Current encryption methods can be computationally expensive, hindering real-time inference and scalability.
Solution
LatticaAI offers a privacy-preserving inference platform that enables AI models to run directly on encrypted data using fully homomorphic encryption (FHE). This approach ensures that user data remains confidential, as neither the AI provider nor the cloud infrastructure can access the raw data. The platform allows AI providers to deploy models without compromising user privacy, while end-users can query models knowing their data stays protected. LatticaAI's platform is built on HEAL (Homomorphic Encryption Abstraction Layer), a hardware-agnostic API designed to bridge the gap between FHE software and specialized hardware, optimizing performance across all layers of the stack. The platform integrates with PyTorch and other industry-standard ML frameworks, enabling seamless adoption without requiring expertise in cryptography.
Target Audience
The primary target audience includes AI providers seeking to deploy models without accessing user data and end-users who require privacy when querying AI models.
Features
- Fully homomorphic encrypted inference, ensuring AI providers never see user inputs, and users never expose their data
- Hardware-agnostic API (HEAL) designed to bridge the gap between FHE software and specialized hardware
- Integration with PyTorch and other industry-standard ML frameworks
- Access control through token-based authentication, allowing AI providers to manage who can use their models
- Compute resource management, enabling control over the computing power used for each model
- Credit-based payment system for compute resources, offering a pay-as-you-go model
- Monitoring dashboard for managing AI models, tracking usage, and controlling access
- Query Client for encrypting input data, sending secure queries, receiving encrypted responses, and decrypting results locally