Chess intelligence, from the board up.
We're building the layer between chess engines and language models: structured chess knowledge, position-aware context, evaluation benchmarks, and chess-specialized models.
Chess Graph
A structured knowledge graph for chess competence.
- Opening repertoire, strategic understanding, recurring patterns
- Interlinked, personalized, and retrievable
- Queryable substrate the entire stack reads from
Chess Context Assembly
Board-state retrieval and analysis.
- Designed for LLM reasoning and explanation
- Surfaces exactly what a model needs from the position
- Not generic RAG pointed at a position
Chess Evals
Benchmarks for chess coaching in language models.
- Scored move by move against expert annotations
- Position-grounded scoring to catch hallucinations
- Level-calibrated, from beginners to titled players
Chess Models
A family of chess-specialized models.
- Chess-language adapters
- Personalized feedback models
- Vision models that read positions from images
Three products, one stack.
The research above becomes products — the foundation model, the knowledge layer, and the agent players talk to, all built on the same stack.
VibeChess Core
A chess-native language model that reads any position and explains the why — not just the best move.
VibeChess Graph
Turns a player's games into a personalized knowledge graph: strengths, gaps, and what to study next.
VibeChess Agent
An agent that teaches — it plays, reviews your games, and answers the follow-up questions.
Backing the lab.
If you back foundational AI work in research labs, eval orgs, or vertical infrastructure, this is the round to be in. Chess is the cleanest place to prove that domain-specific understanding can be engineered, not prompted. If that is a thesis you share, talk to us.