A TRUST ENGINE FOR THE AI ERA
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Introducing enVector
A Trust Engine for the AI Era.
enVector lets you run secure AI workloads using the latest in homomorphic encryption.
PIONEERING AI PRIVACY WITH GLOBAL LEADERS
Most AI leaves your data out in the open.
Enterprises are rapidly deploying AI applications like LLMs, RAG, and biometric authentication to unlock new value.
But most of these applications rely on vector embeddings that can be reverse-engineered to expose sensitive information.
The risks are significant. A single hospital data breach can cost $10M.
92%
of vector embeddings are vulnerable to attack
enVector closes the AI privacy gap.
Powered by the latest in homomorphic encryption, enVector lets you run machine learning models, analytics, and vector searches on sensitive data—while keeping it fully encrypted.
AI Trust Engine: Before & After
Before
Plain Vector Database: Vulnerable to Attacks
After
Protected by enVector: Secure At All Times
What can you do with enVector?
Run quantum-resistant encryption protocols at 1000x the speed of legacy schemes.
Vector memory
Search patient records, financial data, and government information directly on encrypted vectors, keeping sensitive data protected.
Image & facial search
Similar faces or images are matched on encrypted embeddings, with no exposure of the originals.
Vibe coding
Biometric authentication
Fingerprint and face templates are verified under encryption, without disclosing raw data.
Encrypted RAG for LLMs
Chatbots retrieve from encrypted vector stores, preserving document privacy.
Multimodal semantic search
Text, images, and diagrams are compared across encrypted embeddings for secure results.
PIONEERING AI PRIVACY WITH GLOBAL LEADERS
AI trust, without the tradeoffs.
With enVector protecting your data, you can enjoy peace of mind—from pilot to production.
Rapid performance
90x faster encrypted operations than other homomorphic encryption libraries.
Actionable insights
Gain ML/AI insights from sensitive data in healthcare, finance, or genomics while it’s still encrypted.
Total protection
Data is encrypted at-rest, in-use, or in-breach–and if the system is breached, there’s no exposure.
Lower cost
No need for expensive, complicated architecture for data security.
About Heaan
With CKKS inventor Professor Jung Hee Cheon at the helm, our team of 70+ experts, including NIST post-quantum standard contributors, ensures enVector is the trusted choice for privacy-first AI in healthcare, finance, and government. Let AI do all it was meant to, access a new range of insights, and say goodbye to damaging exposure.
Frequently asked questions
What risks does enVector mitigate? v
enVector keeps data encrypted even during in-memory computation, eliminating exposure risks from memory-based attacks, which are becoming ever more pervasive. Since no decryption is required at any stage, it also guarantees strong privacy throughout the search process.
What makes enVector different from a classical vector database? v
Unlike standard vector databases that expose embeddings during search, enVector keeps all data encrypted throughout the entire process. It performs similarity search directly on encrypted vectors using CKKS, ensuring complete privacy and security from end-to-end.
Does enVector work with my existing AI application? v
As long as your AI application utilizes vector searches, you can use enVector to embed technological privacy or security guarantees.
Can I use enVector with Retrieval-Augmented Generation (RAG)? v
Absolutely! enVector was designed for secure RAG. enVector decrypts the vector embeddings to feed into the LLM. Embeddings are never decrypted where they are stored. You can feed private knowledge into LLMs without ever decrypting source documents, queries, or vector embeddings.
What deployment options are available? v
enVector is available as:
- SaaS
- A deployable SDK (for on-prem or cloud-native environments)
- A containerized module for edge or federated deployments
What are the ideal use cases for enVector? v
enVector is built for AI workloads that deal with sensitive vector embeddings, including:
- Encrypted vector memory
- Image & facial search
- Vibe coding
- Biometric authentication
- Encrypted RAG for LLMs
- Multimodal semantic search
Who built enVector? v
enVector is developed by Heaan—the leading inventors of the CKKS encryption company scheme. Our team brings over a decade of experience delivering applied cryptography and AI security at scale.
