Applied Machine Learning & Forecasting

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. We have been doing this work 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: 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. 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.