Regulated enterprises wanted AI. Compliance said the data couldn't leave.
We built a privacy-first enterprise AI platform that runs AI directly on sensitive data — without that data ever leaving the organization's perimeter, even during inference.
The platform supports cloud, hybrid and fully on-premise deployment, with 500+ data-source connectors and AI running under homomorphic encryption.
Every AI solution required sending sensitive data to an external server.
Regulated enterprises in automotive, finance, healthcare and ad-tech wanted to run AI on their most sensitive internal data — but every solution on the market required that data to leave their perimeter for processing. Compliance teams said no. Procurement said no. Legal said no.
The encryption-based workarounds that existed were either too slow for production use or required rebuilding data pipelines from scratch. The market had no ready answer.
Encrypted inference. 500+ connectors. Nothing leaves the building.
Real-Time ETL & Vectorization Pipeline
Ingests, transforms and vectorizes data from 500+ structured and unstructured sources in real time — feeding encrypted knowledge bases without data ever moving to an external server.
Homomorphic Encryption at Inference
RAG, table-augmented generation and NL-to-SQL all run under homomorphic encryption — so the model operates on encrypted data without ever seeing the plaintext.
RAG + TAG + NL-to-SQL
Three retrieval and generation modes supported: retrieval-augmented generation for documents, table-augmented generation for structured data, and natural-language-to-SQL for live database querying.
Zero-Egress On-Premise Deployment
Fully on-premise deployments where nothing leaves the building — no API calls to external models, no telemetry, no external data transfer of any kind.
Cloud, Hybrid or On-Premise
Flexible deployment architecture across all three modes — with the same security guarantees whether running in a private cloud, hybrid environment or air-gapped data center.
Enterprise AI on sensitive data. Zero exposure. Analyzed in place.
Data Source Connectors
Structured and unstructured sources — from cloud data warehouses to document repositories and internal databases — all analyzed in place.
Data Egress On-Premise
In fully on-premise mode, nothing leaves the building — not during ingestion, vectorization, inference or output.
RAG · TAG · NL-to-SQL
Documents, structured tables and live databases — all queryable in natural language, all under encryption.