Custom machine learning services
Our ML team includes PhDs in machine learning, statistics, and computer science with production deployment experience. They bridge the gap between research and real-world applications....
We handle the full ML lifecycle — data exploration, feature engineering, model selection, training, validation, and deployment — using best practices for reproducibility.
We build MLOps infrastructure using MLflow, Kubeflow, or SageMaker to automate model training, deployment, A/B testing, and drift detection.
We combine classical ML techniques with deep learning to create accurate predictive models that integrate into your decision-making workflows.
We build collaborative filtering, content-based, and hybrid recommendation systems that handle millions of users with real-time personalization.
We leverage transformer models, fine-tuned LLMs, and traditional NLP techniques to build solutions that understand and generate human language.
We use ARIMA, Prophet, LSTM, and transformer-based models to deliver accurate time series predictions with confidence intervals.
"Their ML team built a demand forecasting model that improved our inventory accuracy by 40%. It paid for itself in the first month."
Chris Martinez
VP Data, SupplyChainPro
Case Studies
Real results from real projects. See how we've delivered transformative machine learning solutions.
Built a Fraud Detection System Catching 98% of Fraudulent Transactions
Developed a real-time ML pipeline processing 50K transactions/second with 0.01% false positive rate.
Created a Recommendation Engine Increasing Revenue by 25%
Built a hybrid recommendation system serving personalized suggestions to 10M+ users in real-time.
Deployed a Predictive Maintenance System for 10K+ IoT Devices
ML models predict equipment failures 72 hours in advance with 92% accuracy, reducing downtime by 60%.
Tools & Technologies
We combine industry-standard frameworks with modern tooling and proven internal processes to accelerate delivery.
Frameworks
- TensorFlow
- PyTorch
- scikit-learn
- XGBoost
MLOps
- MLflow
- Kubeflow
- SageMaker
- Weights & Biases
Data
- Spark
- Pandas
- Dask
- Feature Store
Deployment
- TensorFlow Serving
- Triton
- BentoML
- ONNX
Frequently Asked Questions
Have more questions? Talk to an expert — we're happy to help.
It depends on the problem. Some techniques work with hundreds of samples, while others need millions. We assess your data and recommend approaches including data augmentation and transfer learning when data is limited.
We implement rigorous evaluation with cross-validation, holdout sets, and fairness metrics. We test for demographic biases and implement mitigation strategies to ensure equitable model behavior.
Yes. We implement automated retraining pipelines that detect model drift, retrain on new data, validate performance, and deploy updated models — all without manual intervention.
We implement monitoring for prediction drift, feature drift, and performance degradation. Automated alerts trigger investigation and retraining, with fallback to previous model versions if needed.
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