Between a model’s general intelligence and the intelligence an enterprise actually needs lies a gap of business context. Closing it is, at bottom, a data problem. The next generation of enterprise AI will not be limited by model intelligence alone. It will be limited by whether enterprises can turn their data into real-time, trusted, and actionable context.
Every contribution Cortex makes to its own state is recorded as something that can be withdrawn. When a fact is taken back, it does not become hidden or deprioritised. It stops being reachable at all.
The smartest thing you can do with Cortex is reduce token usage while saving money on skyrocketing AI inference costs.na8ve

The console draws Cortex as a brain because that is how it is built: nine structures, each doing one kind of work, connected by the tracts the work actually flows through.
Eleven minutes of the console, played at ten times speed. Questions arrive, memories are written and retrieved, the structures light as they work, and the trace underneath records every signal.
The console is not a dashboard bolted onto a database. It follows the datastore itself, reactive and streaming: it redraws as Cortex's state changes, instead of querying a report after the fact.
Where observations arrive. Every write lands here first, as a sensory row, before anything decides what it means.
The mantle: live memories, the durable ones, and those that were retracted or went stale. The shell you see wrapping the brain.
The relay. It opens sessions, retrieves what a turn needs, and assembles the context a question is answered from.
Plans the answer. One plan per turn that retrieved something, written down where you can read it.
What leaves: the model's answer and what it cost to produce, token by token.
Selection and gating. The control signals that start, stop and route the work of the other structures.
Checks the work. It reads sources and evidence in a sealed sandbox and records what it found.
Folds many observations into a standing convention - the pattern behind the events, kept as its own memory.
Consolidation, like sleep: epochs that fold what was learned into the model's own weights.
Percentages are the frame shown in the brain view above.
A brain does not query itself with a "where is my memory" function. It notices, abstracts, and presents what it needs. Cortex keeps memory at three levels and moves it up as it earns its place:
It streams. Memory is updated as observations arrive, and consolidation folds what was learned into the model's own weights - so what it knows changes the way it thinks, continuously.
Retraction is at the heartof Cortex's memory system. Because every contribution is recorded as a change in state, any event can be taken back - and taking it back changes that state of the memory, in real-time.
A retracted memory cannot be retrieved, even by its own vector - verified 16/16.
An organisation evaluating an AI system need more than a certificate, it needs auditability and governance. It wants proof of how it actually thinks. Cortex is built for visibility, which is what makes it auditable - and auditability is the foundation governance stands on.
Cortex provides a AI model for your organisation, not a generic chatbot. Each organisation gets its own learning model with its own memory space. It is kept apart from every other: a search in one organisation's memory cannot draw a single candidate from another's.
Cortex is designed for organizations requiring uncompromising data privacy and cultural alignment. Your organizations model is trained continuously to your specific needs and business domain. This ensures your sensitive information remains secure while empowering your staff with AI tools authentically tailored to your unique institutional identity.
A barebone, open-source agent for your terminal, with long-term memory. It connects to Cortex: it keeps what matters across sessions, answers why questions from what it holds, cites where each answer came from, and lets you withdraw any memory for good.
Early access: there are no official releases yet. Node.js 22.19 or newer, and git.
Each case study is its own repository: a made-up organisation, its records, and one question that no single fact answers. Clone one, seed it into your memory with na8ve agent, and watch the answer come from memory with its citations.
Thirty conditions and a cohort of patients. One arrives tired, with a high ferritin and bronze skin: which explanation ties the findings together?
A bioreactor, a pipetting arm and a chromatography suite streaming live. When the arm raises an alarm, the memory knows what to check.
A small online shop and its code, kept as facts beside the team's decisions. Ask what depends on a function and the answer comes from memory.
A middle school's course as a map of concepts, and every student's records. Why does one student make three different mistakes, and what comes first?
A binder campaign that stalled, eight instruments streaming a culture, and five plates left. The reason is in the record, in pieces.
Everything in them is generated. No real patient, student, shop, lab or instrument is described.