Custom ML models & neural systems.

We build intelligent systems that solve real-world business problems using machine learning and neural networks — from data prep to deployed model.

What we predict, classify, and recommend

📈 Sales forecasting 📦 Demand prediction ⚠️ Churn modeling 🛒 Recommendations 🏷️ Classification 👁 Object detection 🔬 Anomaly detection
Custom prediction models

Forecasts grounded in your data

Sales, demand, churn, fraud — we build, validate, and deploy models that actually generalize. Not just notebooks: production endpoints with monitoring.

  • Time-series forecasting (Prophet, NeuralProphet, Temporal Fusion)
  • Gradient boosting (XGBoost, LightGBM, CatBoost)
  • Tabular deep learning (TabNet, FT-Transformer)
  • Causal inference for "what if" analysis
  • Bayesian models for uncertainty quantification
XGBoost LightGBM Prophet scikit-learn

training run · churn-v3

Converged

Validation loss

0.0891

AUC

0.94

F1

0.89

Recall

0.91

Neural networks

Deep learning, productionized

From CNN-based image classifiers to transformer-based recommenders. We pick the right architecture, train on our GPUs, and ship inference at scale.

  • CNN, ResNet, EfficientNet for vision tasks
  • Transformers for sequence and recommendation
  • Graph neural networks for relational data
  • Multi-task learning for shared representations
  • Quantization & distillation for fast inference

neural-net · viewer

Training
Input · 4 Hidden · 6 Hidden · 6 Hidden · 4 Output · 2
What we provide

From raw data to deployed model

📊

Prediction models

Sales, demand, churn, LTV — robust forecasting with confidence intervals.

🛒

Recommendation systems

Two-tower, matrix factorization, sequence-based — for ecommerce, content, music.

🏷

Classification & detection

Image, text, audio classification. Object detection. Anomaly and fraud detection.

🧹

Data preprocessing

ETL pipelines, feature stores, data validation, drift detection.

Model optimization

Quantization (INT8/FP16), pruning, distillation, ONNX export for fast inference.

🚀

Deployment

Serving via Triton, BentoML, or FastAPI. A/B testing, shadow mode, rollback.

Industries

Built for real-world impact

🏥

Healthcare

Diagnostics, triage, claim prediction

💳

Fintech

Credit scoring, fraud, KYC automation

🛍

E-commerce

Personalization, demand, churn

📚

Education

Adaptive learning, drop-out prediction

Ship a model that actually works

From a vague problem to a production endpoint in 4–6 weeks.