AI memory infrastructure
your security team will actually approve.
When you're building AI agents for thousands of users across departments, you need memory infrastructure that handles isolation, compliance, and scale without becoming a project in itself. That's what Hebbrix is built for.
What changes at enterprise scale
Building a proof of concept is one thing. Running AI memory for production workloads across teams, departments, and compliance boundaries is another.
Data isolation isn't optional
Customer A's data can never appear in Customer B's context. Department boundaries must be enforced, not suggested. At scale, a single context leak is a compliance incident.
Multi-tenancy is the baseline
You're not building for one user. You're building for thousands, each with their own memory space, their own permissions, their own data lifecycle. The memory layer needs to handle this natively.
Performance at scale matters
50ms retrieval with 100 memories is easy. 50ms retrieval with 10 million memories across thousands of concurrent users is an engineering challenge. That's what we've built for.
Built for this
Enterprise requirements aren't bolted on. They're built into the core architecture.
Collections-based multi-tenancy
Every memory belongs to a collection. Collections enforce strict boundaries. One API call creates an isolated memory space for a user, team, department, or customer. No data leaks across boundaries, ever. This is how you serve 10,000 users from one Hebbrix instance without building isolation logic yourself.
Collections documentationMemory-level access control
Flexible scoping lets you define who can read, write, and search which memories. Build role-based access, department-level permissions, or customer-specific memory spaces. The API enforces it. Your application doesn't have to.
Policy documentationFull auditability
Every memory operation is logged. Know who stored what, when it was accessed, and how it was used. Webhooks notify your systems in real time. When your compliance team asks "what data did this agent use to generate that response?", you have the answer.
Webhooks documentationAutomatic data lifecycle
The Ebbinghaus forgetting curve isn't just a feature. It's a compliance tool. Memories that aren't reinforced naturally decay. Combined with explicit retention policies, you get data lifecycle management that works like a real organization, not a database that grows forever.
Memory architectureEnterprise capabilities
API key management
Multiple keys with different permissions
Usage analytics
Monitor memory and search usage across teams
Webhook integrations
Real-time notifications for memory events
Retention policies
Automated data lifecycle management
Collection scoping
Flexible multi-tenant isolation
OpenAI compatibility
Drop-in replacement, no migration pain
Sub-50ms search
Production performance at scale
Python & TypeScript SDKs
Type-safe clients for every team
Ready to add memory infrastructure to your enterprise AI?
Start with the free tier to evaluate. When you're ready to scale, we'll help you architect the right setup for your team.