Discuss your project
TRENDINGMACHINE LEARNING

ML models that perform in production

Building an ML model is only 20% of the work. We focus on the hard 80% — feature engineering, data pipelines, model validation, deployment, and drift monitoring — that makes ML reliable in production.

Discuss your projectAll technologies

Tell us about your project

What we deliver

Core capabilities

  • Feature engineering

    Feature engineering & store design

  • XGBoost, LightGBM,

    XGBoost, LightGBM, neural networks

  • MLflow /

    MLflow / MLOps pipelines

  • A/B model

    A/B model testing frameworks

  • Concept drift

    Concept drift detection & retraining

  • Regulatory model

    Regulatory model validation support

Real-world applications

Use cases

Counterparty Credit Risk Scoring

ML model predicting counterparty default probability integrated into pre-trade limit checks.

Fraud Detection

Ensemble ML model detecting fraudulent payment patterns with real-time inference under 5ms latency.

Price Prediction

Time-series ML models for short-term FX and crypto price prediction used by quantitative traders.

Let's talk

Need ML in production?

Tell us about your project and we'll scope it together — no commitment required.

Get in touch
Layered technology architecture connected through an illuminated gateway