Build with Encrypted Vector Search

Getting started: Check out example notebooks and integration templates at cyborgdb.co/templates

Example Use Cases

• Multi-tenant B2B SaaS: Document search or recommendations where customers demand cryptographic isolation
• Legal tech: Attorney-client privileged communications, M&A due diligence, contract analysis
• HR/recruiting: Candidate matching that doesn't expose salary history or performance reviews
• Supply chain: Optimization using encrypted vendor pricing and contract terms
• E-commerce: Privacy-preserving recommendation engines that can't reconstruct user behavior
• Government: Intelligence analysis, citizen services, inter-agency data sharing

SubTask: CyborgDB Evaluation

• Test CyborgDB in your local system and integrate with your application
• Document any failures or unexpected behaviors and explain why they occur
• Share performance metrics (query latency, throughput, etc.) at the scale of your experiment
• Identify missing features or limitations that could impact real-world deployment

Choose your own application or propose something different—what matters is demonstrating the value of encrypted vector search for sensitive data.

Secure Medical AI with Encrypted Vector Search

Getting started: Check out example notebooks and integration templates at cyborgdb.co/templates

Example Application: HIPAA-Compliant Medical Chatbot

Overview:
Build a secure medical chatbot that answers clinical questions using patient records. Collect and clean EHR/FHIR data while removing or masking PHI. Convert the records into embeddings with a local or private LLM and store them encrypted in CyborgDB. When a clinician asks a question, the system retrieves relevant encrypted embeddings, decrypts them in memory, and generates safe responses. Access controls and logging ensure HIPAA compliance.

Example Tasks

1. Data Pipeline Setup:
Collect and clean medical records while masking or removing PHI before processing.

2. Embedding Generation:
Create encrypted medical text embeddings using a private or local LLM model.

3. Encrypted Storage in CyborgDB:
Store all embeddings and metadata securely in CyborgDB with full encryption.

SubTask: CyborgDB Evaluation

• Test CyborgDB in your local system and integrate CyborgDB and vectors.
• Document any failures or unexpected behaviors and explain why they occur.
• Share performance metrics (query latency, throughput, etc.) at the scale of your experiment.
• Identify missing features or limitations that could impact real-world deployment in clinical settings.

4. Chatbot Retrieval Workflow:
Retrieve encrypted context from CyborgDB and use it to generate safe, clinical responses.

5. Access Control & Auditing:
Implement secure authentication, logging, and audit trails to meet HIPAA standards.

Other Example Applications

• Clinical decision support querying patient histories without exposing PHI
• Medical literature search for researchers handling sensitive trial data
• Symptom checker that can't reconstruct patient information from embeddings
• Insurance claims assistant that protects member data
• Drug interaction checker using encrypted patient medication histories

Choose your own application or propose something different—what matters is demonstrating secure vector search for sensitive medical data.

Fintech Security with Encrypted Vector Search

Getting started: Check out example notebooks and integration templates at cyborgdb.co/templates

Example Application: Real-Time Fraud Detection System

Overview:
Create a real-time fraud detection system that keeps customer financial data encrypted. Transactions are converted into embeddings, stored securely in CyborgDB, and compared against past patterns to detect suspicious activity. The system retrains regularly to improve accuracy and sends alerts to analysts, with all actions logged securely for auditing.

Example Tasks

1. Feature Extraction:
Convert streaming financial transactions into numerical or embedding vectors in real time.

2. Encrypted Indexing:
Insert these embeddings into CyborgDB, ensuring all data remains encrypted end-to-end.

SubTask: CyborgDB Evaluation

• Integrate CyborgDB as your vector store.
• Document any failures or unexpected behaviors and provide reasoning.
• Share performance metrics (query latency, throughput, etc.) at the scale of your test.
• Identify missing features or limitations that could affect real-world fraud detection deployment.

3. Fraud Detection API:
Query CyborgDB for similar patterns to detect suspicious or repeated fraud behaviors.

4. Model Feedback Loop:
Continuously retrain models as new fraud data is collected to reduce false positives.

5. Alert & Review System:
Send high-risk alerts to analysts with secure, encrypted audit logs for traceability.

Other Fintech Applications

• Transaction anomaly detection that can't leak customer financial history
• Account takeover prevention using encrypted behavioral patterns
• Money laundering detection across encrypted customer profiles
• Credit risk modeling that protects borrower financial data
• Investment research platforms with encrypted client portfolio data
• KYC/AML screening that maintains customer privacy
• Payment routing optimization using encrypted transaction patterns

Choose your own application or propose something different—what matters is demonstrating secure vector search for sensitive financial data.