Forecasting models for inventory replenishment, staffing levels, production capacity, and revenue -- trained on your historical data with the signal features that actually drive your specific demand patterns. Model selection based on the time-series characteristics of your data: Prophet for daily/weekly data with strong seasonality and known holidays (promotional periods, public holidays, product launch dates), LightGBM with lag features and rolling window statistics for shorter-horizon forecasting where machine learning outperforms statistical methods, and LSTM/Transformer architectures for complex multi-variate sequences where dependencies between product lines or locations matter. External signals incorporated as model features: weather data (OpenWeather API for weather-sensitive demand), economic indicators (for B2B forecasting), marketing spend calendar (promotional uplift encoded as binary flags), and macroeconomic data for longer-horizon planning. Confidence intervals (5th and 95th percentile prediction bounds) returned alongside the point forecast -- the range your planning team uses to set safety stock levels rather than treating the point estimate as certain. Output delivered on a defined schedule to your ERP (SAP Materials Management, Oracle Inventory, NetSuite), demand planning tool (Kinaxis, Blue Yonder, o9 Solutions), or operations dashboard via API push or direct database write. Forecast accuracy tracked as MAPE and bias by SKU, category, and location -- reviewed monthly and used to trigger model retraining when accuracy degrades beyond configured thresholds.

Churn risk scoring for every customer account, updated on a configured schedule (daily for high-velocity SaaS, weekly for subscription services), and delivered to your CRM (Salesforce, HubSpot, Pipedrive) as a custom field that surfaces in the account view without the retention team needing to run a separate report. Models trained on your historical data across four feature categories: transaction history (purchase recency, frequency, monetary value -- the RFM signals that precede churn across most business models), product usage (login frequency, feature adoption depth, session duration, API call volume -- the engagement signals that predict churn 30-60 days earlier than transaction signals), support interactions (ticket volume, ticket sentiment, unresolved open tickets, CSAT score trend), and contract signals (days until renewal, discount applied at last renewal, contract expansion or contraction history). Feature importance output included with the model so your product and customer success team understands why accounts are at risk -- not just which ones are. Segment analysis identifies which customer cohorts churn earliest: by acquisition channel, plan tier, company size, industry, or geographic market -- informing where retention investment produces the highest return. Retention playbook trigger automation: when an account crosses a configured risk threshold, a task is created in the CRM for the account owner with a suggested action (check-in call, usage review, executive sponsor outreach) based on the account's specific risk factors.

Multi-class classification models for any structured or semi-structured data that your team currently categorises manually at volume. Common applications: document type classification (invoice, purchase order, contract, correspondence -- routing each to the correct processing workflow), support ticket routing (classifying incoming tickets by issue category, product area, and urgency to assign to the right team without a first-line triage step), transaction category labelling (expense categorisation for accounting, transaction type classification for fraud pattern analysis), and lead scoring and qualification classification (SQL/MQL/disqualified based on firmographic and behavioural signals). Model approach matched to data characteristics: gradient boosted trees (XGBoost, LightGBM) for tabular classification where interpretability matters, fine-tuned BERT or RoBERTa for text classification where semantic meaning drives the category, and ensemble approaches when signal comes from both structured fields and text. Confidence score returned with each prediction: high-confidence predictions route directly to the target workflow without human review; low-confidence predictions enter a human review queue with the model's top two candidate classes and the relevant evidence -- typically less than 5-10% of volume at a well-tuned operating threshold. Classification performance evaluated per class (precision, recall, F1) rather than just overall accuracy, because class imbalance in real workflows means overall accuracy is a misleading metric for rare but important categories.

Anomaly detection models for transactions, user behaviour patterns, IoT sensor readings, and operational metrics -- systems that learn your normal baseline and flag deviations that indicate fraud, equipment failure, or process breakdown before they become costly incidents. Unsupervised approaches used where labelled anomaly examples are scarce: Isolation Forest and Local Outlier Factor for tabular data, Autoencoders for high-dimensional time-series sensor data (the reconstruction error serves as the anomaly score -- normal patterns reconstruct accurately, anomalies produce high reconstruction error). Semi-supervised approaches where some historical anomaly labels exist: one-class SVM trained on normal data with known anomaly examples used for threshold calibration rather than training. Statistical process control (SPC) signals (Western Electric rules, CUSUM) used for manufacturing and operations metrics where control chart logic is more interpretable to operations teams than ML model scores. Anomaly types the system distinguishes: point anomalies (single unusual value), contextual anomalies (unusual value for the time of day, day of week, or product type), and collective anomalies (a sequence of individually normal values that collectively indicate an issue). Alert delivery via webhook to PagerDuty, Slack, email, or your operations platform, with supporting evidence: the anomalous value, the expected range for that context, the time window, and similar historical anomalies for comparison. Sensitivity threshold tunable per metric per team: a fraud operations team may want high sensitivity (more false positives acceptable to catch more fraud); an operational alert team may want lower sensitivity to prevent alert fatigue.

NLP systems built on fine-tuned language models calibrated to your domain vocabulary, document types, and entity taxonomy -- not generic pre-trained models applied without adaptation. Document classification using fine-tuned BERT, RoBERTa, or DeBERTa for routing and categorisation: support ticket intent classification (billing inquiry, technical issue, cancellation, feature request), legal document type identification, financial report section tagging, or clinical note category assignment -- each model fine-tuned on your labelled documents rather than relying on zero-shot generalisation. Named entity recognition (NER) for extracting structured data from unstructured text: company names, people, dates, monetary amounts, locations, medical terms (conditions, medications, procedures using UMLS coding), legal parties and clauses, and custom entity types specific to your domain (product SKUs, account numbers, regulatory citations). Fine-tuned on your documents using Hugging Face's transformers library with your custom NER label schema. Sentiment analysis for customer feedback, review monitoring, and support ticket prioritisation: three-class (positive/neutral/negative) or multi-dimensional sentiment (satisfaction, frustration, urgency) depending on the downstream use case. Text summarisation for condensing long documents (contracts, research reports, clinical records) to key points using abstractive summarisation with fine-tuned T5 or BART models. Inference deployed as a FastAPI endpoint -- your application sends text and receives structured JSON output (labels, entities, sentiment scores) in milliseconds.

Image classification, object detection, and defect identification for manufacturing, logistics, retail, and healthcare applications. Models trained on your labelled image data. Integrated into your inspection workflow, camera systems, or document processing pipeline. See our computer vision development page for specific use cases.