Train AI Models · Custom Models

Custom models, built on your data.

When the problem is prediction — risk, demand, churn, fraud — an off-the-shelf API won't cut it. We build and validate models on your data from the ground up, with the accuracy and explainability your risk and compliance teams accept.

Model families

The models we build most.

The right family depends on the question. We choose for accuracy, robustness, and the explainability your domain requires.

Gradient-boosted trees

The workhorse for tabular risk and propensity — high accuracy on structured data with feature importance you can defend.

XGBoostLightGBMMonotonic constraints

Time-series & forecasting

Demand, volume, and load forecasts with seasonality, hierarchy, and calendar effects — reconciled across levels.

HierarchicalProphet-styleQuantile bands

Survival & hazard

When the question is not just if but when — churn timing, time-to-event, and remaining-life estimates.

Cox / AFTDiscrete-timeCensoring

Anomaly & fraud

Catch the rare and the novel — transaction fraud, abuse, and outliers, with graph and sequence signals.

Isolation forestAutoencodersGraph features

Embeddings & ranking

Search, match, and recommend — learned representations that surface the right item, document, or account first.

Two-towerLearning-to-rankVector recall

Uplift & causal

Model the effect of an action, not just the outcome — who to treat, contact, or offer for real incremental lift.

Uplift treesCausal forestsA/B validation
Problem → model

Start with the question, not the algorithm.

“Will this borrower default?”
Gradient-boosted risk model with reason codes
“How much demand next quarter?”
Hierarchical time-series with quantile bands
“Which customers are about to leave?”
Survival model + uplift for who to save
“Is this transaction fraudulent?”
Anomaly detection with graph & sequence features
“Which lead should we call first?”
Propensity model + learning-to-rank
How we build

From a decision to a model that earns.

01 · Frame the decision

We start from the decision the model serves — the action, the cost of being wrong, and the metric that actually matters.

02 · Engineer features

Signal from your systems turned into leak-free features, in a versioned feature store you can reuse across models.

03 · Train & validate

Cross-validation, calibration, and out-of-time testing — so the number holds on tomorrow's data, not just yesterday's.

04 · Explain

Reason codes and SHAP attribution on every prediction, with adverse-action reporting where regulation requires it.

05 · Deploy & monitor

Shipped behind an API with drift detection and scheduled retraining as the world moves underneath it.

Explainability, not black boxes

Every model ships with reason codes and per-prediction attribution, calibrated probabilities, and — for lending and eligibility — adverse-action reporting. A model your team can't explain is a model your regulator won't accept, and we build accordingly.

How we validate models See it in financial services
Get started

Bring us a hypothesis. Leave with a system.

Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.