Our Mission
We are building the persistent memory layer for the artificial intelligence era.
Cortyxia was founded to solve a fundamental flaw in how AI applications are built today: ephemeral context.
Most LLM interactions start from a blank slate. Even with retrieval-augmented generation, context is often fragmented across tools, sessions, and teams. The result is expensive repetition, shallow personalization, and assistants that feel like they have never met the user before.
We act as a memory management layer between your applications and model providers. Cortyxia intercepts, enhances, and persists intelligence across your entire organization—turning isolated calls into a unified corporate memory that grows more valuable with every interaction.
Why we started
In 2025, we watched enterprises deploy AI assistants that could answer questions but could not remember them. Support bots asked the same question in every chat. Sales copilots lost track of deal history. Internal agents rebuilt context from PDFs, tickets, and spreadsheets on every single request.
The cost was not just wasted tokens. It was trust. Users could feel that the system did not know them. We started Cortyxia to fix that at the infrastructure layer.
Our goal is simple: make AI systems that remember what matters, forget what should be forgotten, and hand that control to the people who own the data.
Average token cost reduction when context is persisted and reused across sessions.
Latency for memory retrieval across distributed regions.
Target availability for enterprise memory clusters.
What we believe
Our values are not marketing copy. They are the constraints we use when deciding what to build, what to ship, and what to leave out.
Context First
We believe memory is the missing link in modern AI architectures. Raw compute is cheap, but context is everything. Every feature we build starts with the question: does this make the model understand the user better over time?
Privacy by Design
Enterprise data should not be a black box. We give you absolute control over where your memory lives, who can access it, and how it is used. Encryption, isolation, and auditability are defaults, not add-ons.
Global Scale
Our infrastructure is built to handle millions of interactions per second with sub-200ms latency. Memory should feel local to every user, regardless of geography.
Design principles
Every product decision at Cortyxia is filtered through three non-negotiable principles.
Memory should persist across sessions
A conversation that happened yesterday should inform the one happening today. Cortyxia makes context cumulative, so every interaction builds on what came before.
Data should stay under your control
You choose where memory is stored, how long it is retained, and which systems can access it. We never train on your data without explicit permission.
Infrastructure should feel invisible
Developers should not have to become vector database experts to ship memory-powered features. Cortyxia abstracts the complexity while keeping the system observable and tunable.
Built by researchers and engineers
Our team comes from backgrounds in distributed systems, vector search, and large language model optimization. We have built retrieval platforms, real-time data pipelines, and security-sensitive AI systems at scale.
We are distributed across North America and Europe, and we work with a small set of design partners who push us to keep the product pragmatic, fast, and safe.
If you are solving hard memory or context problems and want to shape what Cortyxia becomes, we would love to talk.
Join the team
We are hiring engineers who care about performance, privacy, and developer experience. If that sounds like you, send us a note.
Ready to make your AI remember?
See how Cortyxia can cut token costs, improve response quality, and give your users an assistant that actually knows them.
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