A TRUST ENGINE FOR THE AI ERA
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IINTRODUCING ENVECTOR
A Trust Engine for the AI Era.
Compute on encrypted data with high-speed FHE.
No decryption. No exposure. No limits.
PIONEERING AI PRIVACY WITH GLOBAL LEADERS
Most AI leaves your data out in the open.
Enterprises are rapidly deploying LLMs, RAG, multimodal AI, and biometric authentication to unlock new value from their data. These initiatives are powered by vector embeddings: mathematical fingerprints of highly sensitive data like patient records, financial histories, and facial features.
The problem is, 92% of embeddings can be reverse-engineered—exposing sensitive data. And it’s only getting worse: the vector database market is expected to grow from $1.5B in 2023 to $6.5B by 2028.
92%
of embeddings can be reverse-engineered
exposing sensitive data
enVector leads the way in AI privacy.
Unlike traditional vector databases that expose sensitive data during decryption, enVector leverages our patented AI Trust Engine™ without exposing data.
Powered by advanced homomorphic encryption, the AI Trust Engine™ lets you run machine learning models, analytics, and vector searches on sensitive data, while keeping it fully encrypted.
AI Trust Engine: Before & After
Before
Vulnerable to attacks
After
Protected by enVector
Peace of mind, from pilot to production.
enVector makes protecting your data easier. It cuts out moments of decryption and operates at 1000x the speed of legacy schemes, without exposing your data to hackers.
1000x faster than legacy schemes
Quantum-resistant encryption protocols
Prevents fines and reputational damage
PIONEERING AI PRIVACY WITH GLOBAL LEADERS
What does it mean in the real world?
Real-world applications powered by enVector’s encrypted AI technology
Encrypted 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.
About Heaan
Founded in 2018 by renowned cryptographer Jung Hee Cheon, Heaan is on a mission to make encrypted computation practical for the AI era. We do this through Fully Homomorphic Encryption (FHE)—specifically, the 4th generation FHE scheme, CKKS.
Every day, our 70-member team of mathematicians, statisticians, data scientists, computer scientists, and hardware and software engineers work to advance this cause in our offices in Seoul, San Jose, and Lyon.
Our flagship innovation, enVector, lets companies compute on encrypted data at speeds fast enough for AI—while maintaining privacy and security at every step.
Empower your business to innovate without limits
Rapid performance
90x faster encrypted operations than other homomorphic encryption libraries.
Powerful 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.
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.
