Last updated: November 2, 2026. Propeliq is engineered from first principles around mathematical confidentiality, zero-trust boundary protection, and absolute client data sovereignty.
Unlike consumer AI providers that route sensitive corporate prompts through multi-tenant shared clouds, Propeliq deploys dedicated, sovereign neural runtime nodes. All model inferences, tensor evaluations, and fine-tuning parameters execute exclusively within client-specified enclaves.
When deployed within your private Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure VPC, or on-premises air-gapped bare metal, zero telemetry or training data ever egresses your security perimeter.
Prior to any data ingestion by our reasoning kernels, our Signal Ingestion Engine applies hardware-level Personally Identifiable Information (PII) redaction. Names, addresses, financial identifiers, and biometric coordinates are mathematically tokenized at the edge boundary. Sensitive source values are restored only within authorized client application layers.
Propeliq platforms are architected to support enterprise compliance certifications:
You retain 100% ownership of your proprietary training corpus, fine-tuned model checkpoint weights, synthesized interface layouts, and business logic. Propeliq makes zero claim to any derivative artifacts generated from your corporate data.
For third-party penetration reports, security architecture whitepapers, or Data Protection Officer (DPO) inquiries, contact: