Custom ML & Model Training
Your data. Your model. Your competitive edge.
General-purpose AI models are powerful, but they do not understand your domain the way a custom-trained model does. We build ML models from the ground up — trained on your data, optimized for your metrics, and maintained for long-term performance.
Core Capabilities
Custom Model Architecture
Design transformer, CNN, RNN, or ensemble architectures tailored to your data modality and task.
Training Pipeline Design
Scalable, reproducible training pipelines on cloud GPUs with experiment tracking.
Data Labeling & Preparation
Data cleaning, augmentation, labeling workflows, and quality assurance for training datasets.
Model Monitoring & Retraining
Automated drift detection, performance dashboards, and scheduled retraining pipelines.
Edge & On-Device ML
Quantized, pruned models optimized for inference on mobile, IoT, and edge devices.
Explainability & Interpretability
SHAP, LIME, and attention visualization for regulated industries requiring model explainability.
Our Process
Data Audit & Strategy
Assess data quality, volume, labeling needs, and define the ML success metrics.
Baseline & Iteration
Build a fast baseline, then systematically improve via feature engineering and model selection.
Training & Validation
Train on your full dataset with cross-validation, hyperparameter tuning, and ablation studies.
Deploy & Maintain
Production deployment with versioning, monitoring, and a scheduled retraining cadence.
What You Can Expect
Technologies We Use
Healthcare Provider — Custom NER Model for Clinical Notes
We built a custom named-entity recognition model trained on de-identified clinical notes that achieved 97% F1-score on medical entity extraction, enabling automated coding at 10× speed.
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Extraordinary?
Let's discuss your project and create a tailored roadmap to bring your vision to life.