Sherpa.ai provides a privacy‑preserving AI platform that lets enterprises, governments, and regulated organizations deploy machine learning models, large language models, and multi‑agent systems across distributed environments while keeping data fully private and sovereign. The platform enables collaborative workflow automation, insight generation, and AI operationalization with built‑in regulatory compliance and risk mitigation. For example, it allows medical researchers to improve diagnostic algorithms without sharing any patient data.
Funding
$35M 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.
ACAHESFounders
Product
Problem
Organizations in regulated sectors face constraints when deploying AI because traditional models require centralizing sensitive data, which raises privacy, security, and compliance risks. Sharing data across departments, partners, or borders can violate regulations and expose proprietary or personal information.
Solution
Sherpa.ai offers a privacy‑preserving AI platform that enables secure, distributed machine‑learning and large‑language‑model deployments without moving raw data. The platform uses techniques such as federated learning, secure multi‑party computation, and encrypted model inference to keep data sovereign and fully under the owner’s control. Enterprises can collaborate on AI projects, automate workflows, and generate insights while meeting regulatory requirements. All AI components are managed through a unified console that enforces security policies, auditability, and compliance reporting. The solution supports a range of AI workloads—from traditional ML models to LLMs and multi‑agent systems—allowing organizations to operationalize intelligence at scale without compromising data privacy.
Target Audience
Primary customers are large enterprises, government agencies, and regulated industries such as healthcare, finance, and energy that need to deploy AI while maintaining strict data privacy and compliance.
Features
- Federated learning and secure multi‑party computation that train models on local data without central data transfer
- End‑to‑end encryption of model inputs, outputs, and intermediate computations
- Centralized governance console for policy enforcement, audit trails, and compliance monitoring
- Support for large language models and multi‑agent AI workflows within a privacy‑preserving framework
- Seamless integration with existing data pipelines and on‑premise, edge, or cloud environments
- Role‑based access controls and data‑sovereignty settings to restrict cross‑jurisdiction data use