Point-in-time safety
Every example is anchored to a cutoff. The model only sees what was knowable at that moment, so there is no forward leakage and a backtest reflects what would really have happened.
We validate graph AI on public benchmarks where the work can be checked, enforce temporal safety so backtests stay honest, and report synthetic-data results as exactly that.
Every example is anchored to a cutoff. The model only sees what was knowable at that moment, so there is no forward leakage and a backtest reflects what would really have happened.
We evaluate on a temporal split of your own data, per prong, and report the held-out metric. Not a training-set score, and not a cherry-picked window.
Uplift is measured on the same split: a local model versus the local model plus the graph engine. You see the delta the graph signal actually adds.
On the public RelBench suite, our graph engine reaches a held-out ROC-AUC of 0.91 on rel-f1 / driver-top3, against a published GraphSAGE baseline of 0.755. RelBench is open, so the result can be reproduced rather than taken on faith.
Benchmarks are easy to dress up. Our standard is simple and fixed:
The goal is a number your data science team can defend in a review, not one that only looks good on a slide.
Upload a labelled table and we will measure uplift on your temporal split, with the held-out numbers in front of you.
We will reach out within one business day.