Ittai Dayan
CEO
Rhinocp
Rhino provides a federated computing platform that lets multiple organizations run AI and analytics workloads on their own edge environments, keeping raw data on‑premise. The system securely aggregates results and enforces policy‑driven governance, enabling collaborative insights without exposing sensitive data.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
- Enterprise Software
- Software Only
Updated 5 months ago
Funding
Raised to date
$17.6MRaised 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.
Funding rounds are not available yet.
Founders
Product
Problem
Organizations often hold valuable data in isolated silos, making it difficult to collaborate on analytics or machine‑learning projects without exposing raw information. This fragmentation hampers the ability to derive comprehensive insights while raising concerns about data security, privacy, and regulatory compliance.
Solution
Rhino provides a federated computing platform that enables multiple parties to run analytics and AI models directly on their own edge environments. By keeping raw data on‑premise, the platform ensures that data never leaves its source, preserving security, privacy, and sovereignty. The system orchestrates distributed computation, aggregates model updates or analytics results, and returns only the derived insights to participants. Built‑in governance controls enforce compliance policies across the network, while the platform’s scalability allows organizations to connect additional nodes as needed, accelerating collaborative value creation without compromising data ownership.
Target Audience
Primary customers are enterprises, research institutions, and government agencies that need to collaborate on data‑intensive projects while keeping their data on‑site and compliant with privacy regulations.
Features
- Edge‑based execution engine that runs AI and analytics workloads on local data stores without data transfer
- Secure aggregation protocol that combines model updates or query results while protecting raw inputs
- Policy‑driven governance layer for privacy, compliance, and data‑sovereignty enforcement across federated nodes
- Scalable network architecture that supports dynamic addition of participants and workload distribution
- Unified dashboard for monitoring federated jobs, performance metrics, and insight delivery