AI engineering
Machine learning
Prediction, classification, and forecasting on your own data — built, measured, deployed, and monitored for the drift that follows.
What this is
The work, described plainly
Not every problem needs a language model. Demand forecasting, defect classification, lead scoring, and anomaly detection are usually better served by a smaller model trained on your history: cheaper per prediction, faster, and far easier to explain to someone who has to act on the output.
The hard part is rarely training. It is the data — how much you have, how it was labelled, what changed in the business halfway through the period it covers, and whether the thing you want to predict was recorded at all.
Models decay. The world moves, the inputs shift, and accuracy falls quietly. Anything we deploy is monitored for drift, because a model nobody is watching is a model nobody should be trusting.
What you get
What is actually handed over
Concrete deliverables, not phases on a timeline.
Data assessment
An honest read on whether your data can support the question, before any modelling budget is spent.
Trained model and baseline
Measured against a simple baseline, because a model that cannot beat a moving average is not worth deploying.
Inference deployment
Batch or real-time, wired into the system that consumes the prediction.
Drift monitoring
Alerts when inputs or accuracy move away from what the model was trained on.
Is this you
Signals this is the right fit
- You have years of operational history nobody analyses.
- People estimate something daily that a model could estimate better.
- You need a number with a confidence attached, not a sentence.
- Explainability matters because someone has to justify the decision.
Typical stack
Chosen per project against your constraints, not applied by default. This is what the work usually involves.
- Python, scikit-learn, PyTorch
- XGBoost and LightGBM
- MLflow
- Pandas, Polars
- FastAPI for serving
FAQ
Questions people actually ask
How much data do we need?
It depends on how many outcomes you are predicting and how noisy they are. Rather than quote a number that will be wrong, we look at your actual data first and tell you whether it supports the question.
Can you explain why the model decided something?
For tree-based models, yes, at the level of which features drove a prediction. If a regulator or an auditor needs that, we choose model families that support it rather than the highest-scoring one.
What happens when accuracy drops?
Monitoring catches it and the model is retrained on recent data. This is normal and expected, which is why retraining is designed in rather than treated as a failure.
Start here
Tell us what is slow, manual, or breaking.
Answer a few questions and get a written brief back — scope, proposed architecture, and what it would take to build.