Forecasting models require careful handling of temporal structure, seasonality, trend decomposition, and lagged feature construction that standard ML pipelines don't address automatically. Vervelo builds time-series forecasting systems using statistical methods (ARIMA, Prophet), machine learning approaches (XGBoost with lag features, LightGBM), and neural sequence models (LSTM, Temporal Fusion Transformer) depending on the data characteristics and forecast horizon. Applications include demand forecasting, patient census prediction, staffing optimization, and operational capacity planning — with prediction intervals and uncertainty quantification as standard outputs.

When labeled data is scarce or the task is to find patterns rather than predict a known target, unsupervised and semi-supervised approaches are required. Vervelo builds anomaly detection pipelines using isolation forests, autoencoders, and statistical process control methods — applied to fraud detection, equipment failure prediction, clinical outlier identification, and data quality monitoring. We also design clustering and segmentation systems (K-means, DBSCAN, hierarchical clustering) for patient cohort identification, customer segmentation, and cohort discovery in clinical research. All outputs include interpretability layers that make model behavior transparent to non-technical stakeholders.

Training large models from scratch is expensive and data-hungry. Transfer learning applies a model pre-trained on large datasets to your specific task, requiring far less labeled data and compute. Vervelo implements transfer learning strategies across vision (ResNet, EfficientNet, ViT), language (BERT, RoBERTa, domain-specific clinical BERT variants), and multimodal models — selecting the right pre-trained backbone, designing the fine-tuning protocol, and validating that the adapted model generalizes to your distribution without overfitting to fine-tuning data. We apply parameter-efficient fine-tuning (LoRA, adapters) when compute budgets are constrained.

A model that performs well in training may be too slow or too large to deploy cost-effectively. Vervelo applies a systematic compression toolkit: structured and unstructured pruning to remove low-importance weights, knowledge distillation to train a smaller student model from a larger teacher, quantization (INT8, INT4) to reduce memory footprint and inference latency, and ONNX export for hardware-optimized deployment. Compression pipelines include automated performance regression testing to verify that accuracy stays within acceptable bounds after each compression step — so you always know the accuracy-efficiency trade-off before making a deployment decision.

Vervelo builds NLP pipelines for text classification, named entity recognition (NER), information extraction, document summarization, and clinical coding. For healthcare, we develop models that extract structured data from unstructured clinical notes — diagnoses, medications, procedures, lab values, and clinical observations — enabling downstream analytics, audit, and automation. NLP systems are built on transformer-based architectures (BERT variants, domain-specific clinical models like BioBERT, ClinicalBERT) and evaluated with entity-level precision, recall, and F1 metrics across document types, note authors, and clinical specialties.

Voice-driven workflows are increasingly common in clinical settings. Vervelo builds speech recognition and audio processing systems for ambient clinical documentation, real-time transcription of patient-provider conversations, speaker diarization (who said what), and voice command interfaces for clinical applications. We fine-tune Whisper and other ASR models on medical vocabulary to handle clinical terminology, drug names, and procedural language that general-purpose transcription models routinely fail on. Transcription outputs feed into NLP pipelines that extract structured clinical data from the resulting text.