Pendra

Pendra is a managed inference platform specifically designed to operate in environments with high security demands and strict regulatory compliance. Its primary function is to enable the secure execution of open-source language models within a controlled jurisdiction, facilitating the adoption of artificial intelligence in critical sectors such as healthcare, legal, government, and finance.

Pendra’s architecture stands out for its technical flexibility, allowing models to be deployed on both the platform's managed hardware and the client's physical infrastructure through the deployment of workers. This custom implementation approach ensures that data processing remains under the direct control of the organization, mitigating risks associated with data sovereignty and the handling of sensitive information.

In terms of operation, the tool functions via a single OpenAI-compatible API, which drastically simplifies model integration without requiring structural changes to the underlying hardware. Pendra provides Python and Node.js SDKs, ensuring seamless compatibility with endpoints from leading providers like Anthropic and OpenAI. The system acts as a centralized orchestration layer that allows development teams to install and call models on their own trusted hardware or utilize hybrid solutions.

Security is the fundamental pillar of Pendra. The platform strictly complies with UK GDPR and offers a zero data retention guarantee. Additionally, it implements end-to-end encryption protocols to protect the integrity and privacy of information throughout the inference process, ensuring that data is never exposed outside authorized boundaries.

Key capabilities and practical benefits include:

• Execution of open-source models on secure and regulated infrastructures.
• Data sovereignty through the ability to use proprietary or managed hardware within a controlled jurisdiction.
• Technical interoperability thanks to its compatible API and support for standard programming languages.
• Privacy guarantee with zero-retention policies and advanced encryption.
• Simplified orchestration of AI resources, allowing the combination of self-managed and cloud solutions under a single workflow.

This solution is particularly useful for companies and agencies that require a secure AI infrastructure and need an inference platform that does not perform model training with their data, focusing exclusively on efficient and protected execution in highly regulated environments.