Enterprise-Ready Platform

The Secure Memory Layer Platform

Build AI applications that respect permissions, enforce consent, and protect sensitive data with military-grade encryption and just-in-time decryption.

Platform Components

Everything You Need for Secure AI

A comprehensive platform that brings together permission management, consent verification, and encryption in one unified solution.

Fine-Grained Access Control

Powered by SpiceDB/AuthZed, our permission engine provides enterprise-grade access control with pre and post-filtering capabilities.

Role-Based Access Control (RBAC)

Define roles and permissions that map to your organization structure

Attribute-Based Access Control (ABAC)

Dynamic permissions based on user attributes and context

Real-Time Permission Syncing

Instant propagation of permission changes across all systems

const retriever = new PolicyMemoryRetriever (
permissions: 'spicedb',
preFilter: true,
postFilter: true,
denyByDefault: true
);

Purpose-Driven Data Access

Integrate with leading consent platforms to ensure every data access respects user preferences and regulatory requirements.

OneTrust

OneTrust Integration

Securiti

Securiti Compatible

TrustArc

TrustArc Support

Transcend

Transcend Ready

Marketing Analytics
Consented
Last updated: 2 hours ago
Product Improvement
Consented
Last updated: 1 day ago
Third-Party Sharing
Denied
Last updated: 5 days ago

Military-Grade Encryption

Protect sensitive data with AES-256 encryption, field-level security, and just-in-time decryption capabilities.

AES-256
Encryption Standard
<50ms
JIT Decryption
Field-level encryption for PII data
Ephemeral key management
Cloud KMS integration (AWS, GCP, Azure)

Encryption Pipeline

Data Input
AES-256 Encryption
Encrypted Storage

Native Framework Integration

Get started in minutes with our comprehensive SDKs and pre-built components for popular AI frameworks.

Python SDK TypeScript SDK LangChain LangGraph RESTful API
quickstart.py
from policymemory import PolicyMemoryRetriever
from langchain import LLMChain

# Initialize with your configuration
retriever = PolicyMemoryRetriever(
    api_key="your-api-key",
    permissions="spicedb",
    encryption=True
)

# Use with LangChain
chain = LLMChain(
    retriever=retriever,
    llm=your_llm
)

# Secure retrieval with user context
results = chain.retrieve(
    query="patient symptoms",
    user_id="doctor_123",
    purpose="treatment"
)
Architecture

How It Works

A multi-stage pipeline that ensures security and compliance at every step of the retrieval process.

1

Permission Check

Verify user permissions against SpiceDB

2

Consent Verification

Check consent status for data purpose

3

Vector Search

Similarity search on permitted data

4

Post-Filter

Validate results against policies

5

JIT Decrypt

Decrypt only authorized fields

6

Return Results

Deliver secure, compliant data

Integrations

Works With Your Stack

Seamlessly integrate with your existing tools and platforms for a unified security layer.

AI Frameworks

LangChain LangGraph LangSmith OpenAI

Vector Databases

Milvus Pinecone Weaviate Qdrant

Authorization

SpiceDB AuthZed Okta Auth0

Privacy & Consent

OneTrust Securiti TrustArc Transcend
Security & Compliance

Enterprise-Grade Security

Built to meet the strictest security and compliance requirements for regulated industries.

SOC 2 Type II

Certified

HIPAA

Compliant

GDPR

Compliant

ISO 27001

Certified

PCI DSS

Level 1

CCPA

Compliant

Ready to Secure Your AI?

Start building permission-aware RAG applications with enterprise-grade security today.