Zero Data Retention

We do not store or train on your private data. All processing is ephemeral and encrypted. This means that any information you share with us is handled with the utmost care and security. Once your query is processed, the data is not retained or used to improve our models—ensuring your privacy remains intact. The encryption safeguards your data during transmission and processing, making it inaccessible to unauthorized parties.

On-Premise Deployment

Running Lurk entirely within your infrastructure can enhance security and control by keeping all operations and sensitive data confined to your own environment. This approach isolates the system from external networks, reducing exposure to potential cyber threats and unauthorized access. By leveraging Lurk's architecture—such as its Cryptographic Service and Service Instance model—you can ensure that cryptographic material and processing remain within a trusted boundary, using hardware security modules if needed.

SLA-Guaranteed Uptime

Achieving 99.99% uptime with enterprise-grade support and incident response ensures your system remains operational with minimal downtime—approximately 52 minutes per year—meeting the stringent demands of critical business applications. This level of reliability relies on robust infrastructure, such as redundant data centers and automated failover mechanisms, as seen in solutions like Microsoft Entra, which maintains this uptime through backup authentication systems and multi-region resilience. Enterprise-grade support provides 24/7 expert assistance, rapid incident detection, and resolution, often reducing mean time to recovery (MTTR) significantly—some providers achieve a 74% reduction in response times.

Custom Fine-Tuning

Training agents on your domain-specific data with full auditability and version control allows you to customize AI models to your unique needs while maintaining transparency and traceability. This process involves feeding your proprietary data—such as industry-specific documents or datasets—into the training pipeline, enabling the agents to learn contextually relevant patterns.