Everyone is building context for machines. Nobody is building it for you.
Every AI company on Earth is racing to build the same thing: context layers for machines. RAG pipelines, vector stores, memory systems. Billions of dollars spent so the AI can know more about you, remember more about you, predict more about you.
Nobody stopped to ask the inverse question.
What if you could build that same structured context for yourself? Your own thinking, mapped into a graph you can navigate. Your own patterns, surfaced before they fade. Your own blind spots, visible for the first time. Not so a machine can serve you better. So you can think better.
That's Ryzome. A semantic memory graph with AI-assisted structuring and a schema you define. It doesn't ask you to open a new app or learn a new workflow. It's an MCP server that connects underneath the AI tools you already work with. Claude Code, ChatGPT, Cursor. You keep thinking where you think. Ryzome structures it all into a knowledge graph behind the scenes.
You talk to your AI the way you always do. The graph builds itself.
A semantic memory graph with two lenses.
Ryzome is a knowledge graph with AI-assisted structuring and a schema you define yourself. It captures two kinds of knowing, and connects them in a single navigable space.
Cognitive mode: How you think.
Thoughts, decisions, patterns, insights, events. The subjective, temporal, personal layer. The stuff that lives in your head and usually dies there.
A researcher adds "hypothesis." A writer adds "draft" and "revision." An analyst adds "assumption" and "signal."
Relational mode: The things you think about.
Entities, edges, constraints. The structured, typed, precise layer. The people, companies, technologies, opportunities, and problems that populate your world.
A sales engineer adds "opportunity" and "requirement." A policy analyst adds "constituent concern" and "legislative window."
Where they meet is where it gets powerful.
A decision captured in cognitive mode links to the customer it references in relational mode. A pattern you noticed connects to the technology it applies to. A hypothesis links to the evidence that supports or undermines it. One graph. Multiple lenses. Emergent connections that no siloed tool can produce.
This is meaning first, data second. Traditional knowledge management is archaeology. You dig through information hoping to find meaning. Ryzome is architecture. You capture meaning directly, and the structure builds itself around it.
How it works.
Three steps. Zero new apps.
Use your AI tools.
Claude Code, ChatGPT, Cursor. Whatever you think with already. Ryzome is an MCP server that plugs into the tools you use every day. You don't come to Ryzome. Ryzome comes to you.
Define your schema. AI conforms to it.
Tell Ryzome how your world is structured. Add node types, relationships, constraints. Once your schema is set, the AI structures every incoming thought to match it. Automatically. Every time.
Watch your graph grow.
Entities get resolved. Relationships get mapped. Patterns emerge you didn't see coming. And when you want the full picture, the visualization layer shows you your thinking as a navigable, queryable, living graph.
Your schema. Your language. Your graph.
Every mind organizes differently. Every domain has its own vocabulary. Ryzome ships with five base cognitive types: thought, decision, pattern, insight, event. But the real power is that you define the rest.
A clinical researcher adds "hypothesis," "protocol," and "adverse event." A sales engineer adds "opportunity," "stakeholder," and "competitive threat." A product manager adds "signal," "bet," and "user story." Same infrastructure. Different lenses. Each one precisely tuned to how that person's work actually moves.
And here's what matters: these aren't just labels. They're schema. When you add "opportunity" as an entity type, the AI knows to extract opportunities from your conversations. When you define an edge constraint that says opportunities connect to stakeholders, the AI maps those relationships automatically. Your ontology becomes the AI's instruction set.
Researcher
- Hypothesis
- Protocol
- Adverse Event
- Experiment
- Finding
Sales Engineer
- Opportunity
- Champion
- Risk
- POC
- Tech Stack
Second Mind
- Thought
- Decision
- Pattern
- Insight
- Event
The connections across domains are where it gets interesting. When a technical pattern informs a sales approach. When a customer insight shapes a product decision. When a regulatory change links to an engineering constraint. That cross-pollination is what no siloed tool can give you. And it happens naturally in Ryzome, because it's all one graph.
The collective layer.
Today, Ryzome is individual. Your graph, your sovereignty, your thinking made visible. But the architecture was built for what comes next.
Imagine multiple people in an organization, each building their own cognitive graph. Each defining their own schema. Each capturing what they actually think, not what they perform for meetings. Now imagine they can consciously choose to surface specific nodes to a shared organizational space. Nothing scraped. Nothing auto-shared. Every contribution is a deliberate act.
The enterprise sees what people are independently converging on. If twelve people across four teams are circling the same technical constraint without knowing it, that signal surfaces. No survey required.
And beyond the collective: a schema marketplace. Cognitive templates from people in similar roles. A PhD researcher publishes their hypothesis framework. A veteran product manager shares their prioritization model. New users don't start from zero. They fork a template, adapt it, and build from there.