BeeKeeperAI offers EscrowAI, a secure collaboration platform that lets AI developers run encrypted models on HIPAA‑compliant data within a data steward’s protected environment, while keeping both the data and the algorithm invisible to the other party. Using hardware‑based secure enclave technology and end‑to‑end encryption, the platform enables real‑world model validation without exposing sensitive patient information or intellectual property, accelerating development cycles and reducing costs for regulated industries.
Funding
$12.1M 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
Developing and deploying reliable AI models for regulated domains such as healthcare often requires years of work, millions of dollars, and exposes both the training data and the algorithm to privacy and intellectual‑property risks.
Solution
BeeKeeperAI’s EscrowAI platform creates a secure collaboration environment where AI developers can submit their models to run directly on a data steward’s protected infrastructure. Both the data and the algorithm remain encrypted at all times, leveraging hardware‑based secure enclave technology to prevent any party from viewing the other’s assets. The platform enables computation on real, HIPAA‑compliant data rather than synthetic or de‑identified substitutes, preserving model fidelity while maintaining strict privacy guarantees. Results are returned to the developer without exposing the underlying data, and the data steward retains full control over their information. This approach shortens development cycles, reduces costs, and safeguards intellectual property throughout the validation and deployment process.
Target Audience
Primary users are AI developers and model owners seeking to validate algorithms on sensitive healthcare data, and data custodians (hospitals, research institutions, and regulated enterprises) that need to protect patient privacy and proprietary information.
Features
- End‑to‑end encryption of the AI model during upload, transit, and execution within the data steward’s environment
- Secure enclave execution that isolates compute workloads, eliminating risk of data exfiltration or algorithm interrogation
- Direct processing of primary, HIPAA‑compliant data sources, avoiding reliance on synthetic or de‑identified datasets
- No data or model visibility across parties; only aggregated results are shared back to the developer
- Compliance‑ready architecture supporting healthcare and other regulated industries
- Integrated audit logs and access controls for traceable, governed collaborations