What is a graph foundation model?
A model pretrained to predict over relational, connected data, so it can answer new predictive questions on tables it has never seen, without task-specific training.
Resources
Learn the ideas behind graph AI.
Short, plain explainers of the concepts that make production-grade graph AI work. No jargon for its own sake.
Start with the fundamentals.
How relational data becomes a graph
Rows are entities, foreign keys are edges, and timestamps order events. Your existing schema already describes a temporal graph; nothing new to model by hand.
Declarative predictive queries
A query says what to predict, for which entity, over what horizon. AhinsaAI compiles it into an autonomous agent that runs the query and takes the action, no feature engineering required.
Point-in-time safety
Anchoring every example to a cutoff means the model only sees what was knowable then. It is what separates an honest backtest from a leaky one.
Graph AI vs. traditional ML
Hand-built pipelines flatten relationships into features and lose multi-hop signal. Graph AI learns across tables directly, so the signal between rows survives.
Backtesting, honestly
A held-out temporal split, reported per prong, tells you what would really have happened. A training-set score does not.
Ready to try it on your data?
Bring a labelled table or use a bundled sample, and we will run a use case end to end.
We will reach out within one business day.