///Future of work
AI is stateless. We fix that.
Cortex provides the developer tools and infrastructure to build, maintain, and scale an agent’s context and memory layer. This enables agents to persist context, interactions, and outcomes across sessions. We make AI stateful.
Enterprise context retrieval is failing. So is AI.
1.
Teams building AI systems start with vector databases.
That works for a while. Early prototypes retrieve a few relevant documents, prompts stay manageable, and the system appears reliable.
But as the knowledge base grows, cracks begin to appear.
Flat vector indexes struggle with evolving user context, temporal correctness, and multi-session reasoning. Retrieval becomes noisy. Older context resurfaces when it should not. New information fails to override outdated state.
Over time the database turns into a soup of embeddings that degrades recall accuracy and reliability.
2.
To compensate, teams add more layers.
Knowledge graphs.
Rerankers.
Caching systems.
Custom memory services.
What began as a simple retrieval stack slowly becomes a patchwork of pipelines trying to approximate continuity.
3.
Many new “AI memory” products attempt to solve this.
But most are thin abstractions built on top of vector databases. They store user inputs, summarize conversations, and retrieve them later as embeddings.
Underneath, the same limitations remain. Memory becomes another retrieval system rather than a system that understands how state evolves.
4.
The deeper issue is that context and memory are treated as separate systems.
Context is used to decide what information should be retrieved.
Memory is used to store what happened before.
But intelligence depends on both working together.
5.
Memory explains why something matters.
Preferences, past outcomes, decisions, and feedback teach a system how to interpret new information. They shape how context should be ranked, interpreted, and used.
Without memory, context selection becomes guesswork.
6.
Context determines what matters right now.
Agents must retrieve the right signals from large knowledge stores, understand how those signals relate to the current situation, and adapt as new information appears.
Without the right context, memory alone cannot guide decisions.
7.
Production AI requires both working as one system.
Agents must remember users, track evolving state, understand outcomes, and use those signals to interpret future context. Intelligence compounds when systems can connect past experience to present decisions.
This is not retrieval. It is state.
8.
Cortex exists to solve the context problem.
We provide the infrastructure developers use to build persistent context stores for AI systems. Context, interactions, and outcomes live in the same evolving system rather than fragmented pipelines.
Agents maintain continuity across sessions and learn from results.
9.
Instead of flattening information into embeddings, Cortex models context as evolving state.
New information creates new versions of state rather than overwriting the past. Context decays when it stops being relevant. Important signals remain accessible.
This allows agents to reason about what changed, when it changed, and why it matters.
10.
When context compounds, intelligence compounds.
Agents become more consistent. Systems adapt to users. Outcomes improve because every interaction contributes to future decisions.
11.
We believe every agent will eventually have its own context store.
Just as databases became foundational infrastructure for software, persistent context will become foundational infrastructure for AI.
Cortex is building the infrastructure that makes this possible.
Enterprise context retrieval is failing. So is AI.
1.
Teams building AI systems start with vector databases.
That works for a while. Early prototypes retrieve a few relevant documents, prompts stay manageable, and the system appears reliable.
But as the knowledge base grows, cracks begin to appear.
Flat vector indexes struggle with evolving user context, temporal correctness, and multi-session reasoning. Retrieval becomes noisy. Older context resurfaces when it should not. New information fails to override outdated state.
Over time the database turns into a soup of embeddings that degrades recall accuracy and reliability.
2.
To compensate, teams add more layers.
Knowledge graphs.
Rerankers.
Caching systems.
Custom memory services.
What began as a simple retrieval stack slowly becomes a patchwork of pipelines trying to approximate continuity.
3.
Many new “AI memory” products attempt to solve this.
But most are thin abstractions built on top of vector databases. They store user inputs, summarize conversations, and retrieve them later as embeddings.
Underneath, the same limitations remain. Memory becomes another retrieval system rather than a system that understands how state evolves.
4.
The deeper issue is that context and memory are treated as separate systems.
Context is used to decide what information should be retrieved.
Memory is used to store what happened before.
But intelligence depends on both working together.
5.
Memory explains why something matters.
Preferences, past outcomes, decisions, and feedback teach a system how to interpret new information. They shape how context should be ranked, interpreted, and used.
Without memory, context selection becomes guesswork.
6.
Context determines what matters right now.
Agents must retrieve the right signals from large knowledge stores, understand how those signals relate to the current situation, and adapt as new information appears.
Without the right context, memory alone cannot guide decisions.
7.
Production AI requires both working as one system.
Agents must remember users, track evolving state, understand outcomes, and use those signals to interpret future context. Intelligence compounds when systems can connect past experience to present decisions.
This is not retrieval. It is state.
8.
Cortex exists to solve the context problem.
We provide the infrastructure developers use to build persistent context stores for AI systems. Context, interactions, and outcomes live in the same evolving system rather than fragmented pipelines.
Agents maintain continuity across sessions and learn from results.
9.
Instead of flattening information into embeddings, Cortex models context as evolving state.
New information creates new versions of state rather than overwriting the past. Context decays when it stops being relevant. Important signals remain accessible.
This allows agents to reason about what changed, when it changed, and why it matters.
10.
When context compounds, intelligence compounds.
Agents become more consistent. Systems adapt to users. Outcomes improve because every interaction contributes to future decisions.
11.
We believe every agent will eventually have its own context store.
Just as databases became foundational infrastructure for software, persistent context will become foundational infrastructure for AI.
Cortex is building the infrastructure that makes this possible.
Frequently Asked Questions
Common operational inquiries regarding architecture, security protocols, and deployment strategies.
Why can't I build it myself?
What are the alternatives to Cortex?
Can I deploy on-premise?
What is the API latency?
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What happens if I hit the rate limit?
Frequently Asked Questions
Common operational inquiries regarding architecture, security protocols, and deployment strategies.