Helping our customers build Confidential AI
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Get Started
1
About You
2
Install CyborgDB
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For evaluation & non-commercial use, start now:
bash
pip install cyborgdb_lite
Developer Docs
OR
For commercial deployments, get in touch with us:
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Unlock the Power
of Confidential AI
PatientID: #894512
Diagnosis: Hypertension
TradeOrder: Buy 500 AAPL
Portfolio: $2.1M Assets
API_Key: ak_live_xz31
Model_Weights_v3
Transaction: $22,300
Prescription: Atorvastatin
InvestorTermSheet.pdf
SSN: 123456789
SSN: 123456789
PatientID: #894512
Diagnosis: Hypertension
TradeOrder: Buy 500 AAPL
Portfolio: $2.1M Assets
API_Key: ak_live_xz31
Model_Weights_v3
Transaction: $22,300
Prescription: Atorvastatin
InvestorTermSheet.pdf
SSN: 123456789
SSN: 123456789
PatientID: #894512
Diagnosis: Hypertension
TradeOrder: Buy 500 AAPL
Portfolio: $2.1M Assets
API_Key: ak_live_xz31
Model_Weights_v3
Transaction: $22,300
Prescription: Atorvastatin
InvestorTermSheet.pdf
SSN: 123456789
SSN: 123456789
The Missing Piece
for Confidential AI
The Confidential Vector DB
CyborgDB is the only Vector DB that keeps inference data end-to-end encrypted—at rest, in transit, and in-use
Your DB, Made Confidential
Instantly transform existing databases like PostgreSQL, Redis, and RDS into Confidential Vector Databases
Get Started in Minutes
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from cyborgdb_lite import Client, IndexIVFFlat
# Create a Redis-backed client
client = Client("redis", "redis://localhost")
key = secrets.token_bytes(32)
index = client.create_index("my_index", key, IndexIVFFlat(128, 1024))
from cyborgdb_lite import Client, IndexIVFFlat
# Create a Redis-backed client
client = Client("redis", "redis://localhost")
key = secrets.token_bytes(32)
index = client.create_index
("my_index", key, IndexIVFFlat(128, 1024))
#include "cyborgdb_core/client.hpp"
#include "cyborgdb_core/encrypted_index.hpp"
cyborg::Client client(DBConfig(cyborg::Location::kRedis, "redis://localhost"));
std::array<uint8_t, 32> key; //Replace with your encryption key
auto index = client.CreateIndex("my_index", key, cyborg::IndexIVFFlat(128, 1024));
#include "cyborgdb_core/client.hpp"
#include "cyborgdb_core/encrypted_index.hpp"
cyborg::Client client(DBConfig
(cyborg::Location::kRedis, "redis://localhost"));
std::array<uint8_t, 32> key;
auto index = client.CreateIndex
("my_index", key, cyborg::IndexIVFFlat(128, 1024));
Use Cases
Confidential Semantic Search
Securely leverage similarity search for text, image, audio and more while guaranteeing privacy
Confidential RAG
Securely combine Private data retrieval with Gen.AI, enabling Confidential AI applications
HIPAA-Compliant RAG
Leverage Gen. AI and end-to-end encrypted vector retrieval for HIPAA-compliant RAG
Similarity Search on PHI
Enable similarity queries on multimodal Protected Health Information in full compliance
Multi-Party Fraud Detection
Leverage encrypted vector search for secure multi-party fraud detection model inference
Banking Data RAG & Agents
Offer personalized and context-augmented Gen. AI products while safeguarding financial data
