AI NATIVE RESEARCH COMPANY
Before language, there was a world.
We build AI that understands the world before it speaks.
01 — WORLD MODEL
Predict first. Speak later.
A child knows where a ball will roll long before learning the word for it. We research pre-linguistic world models: models that learn cause and physics from the world itself, not from text, and imagine the next moment.
02 — CONTEXT GRAPH · AGENT HARNESS
Context becomes a world.
Scattered information is not intelligence. A context graph weaves concepts and relations into one living structure; an agent harness lets AI plan, act and verify itself on top of it. We design this foundation ourselves.
No connected concepts yet.
Type a sentence or pick an example. Drag nodes to explore.
- Agent
- Tool
- World
- Context
- Goal
- Entity
03 — AGENTS
We teach machines to imagine worlds.
Given a goal, an agent plans, uses tools and checks its own work. When it fails, it plans again. When many agents share one world, that world starts to move on its own.
- plan
- act
- verify
- done
- human review
Pick a goal and hand it to an agent.
04 — APPLICATIONS
From research to worlds people live in.
- 01 · AX
Rewriting how work works
Agents understand an organisation's context and restructure workflows themselves. Not bolting on tools, but making the work itself AI-native.
- 02 · GAME AI
Opponents that feel alive
NPCs that read the situation and change tactics instead of following a script. Game intelligence that gives every player a different experience.
- 03 · VIRTUAL WORLDS
Worlds that run themselves
Virtual worlds built on world models and agents keep evolving after people log off. The next step for simulation, metaverse and digital twins.
05 — ROADMAP
From the foundation to whole worlds.
- STAGE 1 · NOW
Context graph and agent harness
The layer every later stage stands on: context woven into structure, and a loop in which agents plan, act and verify. The concept demos on this page are where it starts.
- STAGE 2 · NEXT
World models for simulation and games
Pre-linguistic world models that learn cause and physics in simulated environments, applied first to game AI.
- STAGE 3 · LATER
Virtual worlds that evolve themselves
Worlds where world models and agents move together, with the same stack extended to organisational AX and digital twins.
06 — WORK WITH US
How we build together
Joint research
With universities and institutes, on world models and on how to evaluate agents.
Proof of concept
Apply the agent harness to one real workflow in your company and measure the effect together.
Co-development
With game and virtual-world studios, building NPC intelligence and world simulation.
CODEMINGO
Nothing is impossible. Only not yet modeled.
We are looking for partners to build the next world with.
Start a conversationhello@codemingo.com