Custom ML Model Development
We build custom ML models for structured data such as transactions, claims, orders, patient records, and sensor readings. For classification, scoring, and ranking, our teams often use gradient-boosted trees such as XGBoost and LightGBM. We also work with your domain experts to define the right features, because the signals an adjuster, clinician, or dentist already relies on can be critical to model performance.
Deep Learning Development
Our approach uses deep learning when the data is large, unstructured, or sequential, such as images, audio, long text, clickstreams, and sensor time series. We train models with PyTorch and TensorFlow, starting with pretrained networks where suitable, and recommend deep learning only when its performance gains justify the added compute and lower explainability compared with a simpler baseline.
NLP Development
We turn text into structured data your systems can act on, from classifying tickets and claims to extracting entities from notes, scoring sentiment, and routing messages. Our teams fine-tune transformer models on your labeled text and use smaller models when processing volume is high. When a task needs open-ended reading or drafting, we recommend a language model instead of forcing a classifier to do the job.
Recommendation Engines
Our teams build recommendation engines that learn from views, saves, purchases, and replies, combining user behavior with item attributes to decide what each person sees next—a product, match, article, or plan. We account for new users and items so the system can still make sensible suggestions with limited history. Before replacing an existing approach, every engine must outperform a simple popularity baseline in a live A/B test.
Fraud Detection ML
We build fraud detection models that score each transaction, claim, or account in milliseconds, letting clear cases pass, stopping likely fraud, and sending uncertain cases to an analyst with reasons attached. Our models are designed for heavily imbalanced data, with thresholds set around the cost of missed fraud versus blocking a legitimate customer, not raw accuracy alone.
Anomaly Detection
Our experts build anomaly detection models for cases where few labeled examples exist, learning what normal looks like for each asset or account and flagging meaningful departures. This can include a machine drifting out of range, a spike in refunds, or an unusual login. Each alert includes the underlying readings or signals so your team can understand why it was flagged.
ML Model Integration
We integrate ML models into the systems where their predictions drive real decisions as real-time APIs, batch jobs that write scores into your CRM, EHR, or ERP, or components inside your app. Each integration includes fallbacks, prediction logging, and the explanation fields your users and auditors need.
