# Applied Machine Learning & Forecasting URL: https://momentaj.com/services/applied-machine-learning/ Updated: 2026-10-09 Category: AI services Provider: Momentaj Inc., Toronto AI engineering firm; remote across Canada, the United States and the Middle East, in person across the Greater Toronto Area Forecasting, scoring, clustering and anomaly detection on your own data, in Python on PyTorch and TensorFlow, with the data pipelines that make the models trustworthy. At a glance: - Time-series forecasting and demand models - Scoring, segmentation and anomaly detection - ETL and a modelled database before any model - Models your team can retrain and explain Applied machine learning is the use of forecasting, scoring, clustering and anomaly-detection models on a company's own data, for questions about numbers over time, categories or anomalies. Momentaj, a Toronto AI engineering firm, builds these models in Python, with the data pipelines that make them trustworthy. Not every problem needs a language model. Forecasting demand, scoring opportunities, finding the anomaly in a sensor stream or segmenting a market are machine-learning problems with twenty years of good method behind them. Our founder has worked on machine-learning problems since 2014, and we start every one of these projects with the data, not the model. ## Who it's for - Businesses with transaction, market, sensor or operational data and questions about what happens next - Teams whose spreadsheets have become models that nobody can retrain - Products that need a score, a forecast or an alert inside them ## What we build - **Data pipelines.** Ingestion from files and APIs, cleaning and enrichment, and a modelled database with lookups and fact tables, so every model runs on data that means what it says. - **Forecasting.** Time-series models from exponential smoothing and ARIMA to recurrent neural networks, chosen by measured accuracy on your series, not by fashion. - **Scoring and recommendation.** Predictive scores, ranking and recommendations, with the explanation of why. - **Clustering and anomaly detection.** Market segments, customer groups, sensor faults, discrepancies in transaction logs. - **Classification on signals.** Positioning from radio signal strength, device-state detection, document and message classification. ## How it's done Exploratory analysis first, so we know what the data can and cannot support. Then a baseline, then better models only where they beat it. Everything is reproducible: versioned data, versioned features, versioned models, and a report your team can read. Python, scikit-learn, PyTorch, TensorFlow and Keras; R where it fits. ## Proof [Valco AI](https://momentaj.com/case-studies/valco-ai/): the Dubai Land Department's open property records turned into a modelled market database, forecasting with recurrent neural networks, clustering, anomaly detection and investment scoring, served through a conversational interface and a dashboard. The pipeline, the models and the order we built them in are written up in [From public data to market intelligence: how Valco AI was built](https://momentaj.com/insights/from-public-data-to-market-intelligence/). Our indoor-positioning research reached 85% zone accuracy from BLE signal strength with a random forest, and the reconciliation engines in our founder's banking history matched transactions across more than fifty file formats. ## Engagement shape A two-week data assessment with exploratory analysis and a baseline, then modelling in fixed increments. See [how an engagement works](https://momentaj.com/how-we-work/). ## Frequently asked questions **Should we use an LLM for forecasting, scoring or anomaly detection?** If the question is about numbers over time, categories or anomalies, a classical or deep-learning model will usually be cheaper, faster and more accurate, and it can be explained. We use language models where language is the input or the output. **Is messy data a blocker for a machine learning project?** Messy data is the normal starting point. Cleaning and modelling the data is the first half of the project and the part that decides whether the model can be trusted. **Can our team maintain the machine learning models?** Yes, and the handover is designed for it: we deliver versioned pipelines, documented features and retraining procedures, and we train your team to run them. **How do you choose a machine learning model?** Each model is measured against a baseline on your own history, and it earns a place only if it beats that baseline. For Valco Properties, recurrent networks earned it on some series and not on others. **How much does an applied machine learning project cost?** A fixed price per stage, agreed before it starts. It begins with a discovery of one to two weeks that looks at your data and systems, because cleaning and modelling the data is the first half of the work. **Do you work with companies outside Toronto?** Yes. We work remotely with clients across Canada, the United States, the Middle East and beyond, and in person across the Greater Toronto Area.