Machine Learning & Data Engineering
Models that reach production, stay monitored, and are honest about what they cannot predict.
Overview
The short version
The gap between a notebook that scores well and a service your business depends on is where most machine learning projects quietly end. We work backwards from deployment: what data is actually available, how often it arrives, what accuracy is worth paying for, and what happens when the model drifts.
Technologies we use here
Chosen per project against your team's existing skills, your hosting constraints and long-term maintenance cost — not house preference.
Scope
What we build
Predictive models
Forecasting, churn, demand, risk scoring and classification on structured business data.
Computer vision
Detection, classification, OCR and quality inspection pipelines.
Natural language systems
Classification, extraction, summarisation and semantic search over text you already hold.
Recommendation systems
Ranking and personalisation for commerce, content and internal matching problems.
Data pipelines
Ingestion, cleaning, feature stores and the scheduled jobs that keep the whole thing fed.
Method
How we approach it
The same three commitments apply to every engagement, whatever the technology involved.
Feasibility first
A short paid assessment tells you whether your data can support the outcome you want. If it cannot, you will hear that in week one rather than month four.
Baseline before complexity
A simple model and a clear metric come first. Deep learning is used when it earns its cost, not to make a proposal sound impressive.
Monitored after deployment
Drift detection, input validation and retraining triggers, because a model's accuracy is a moving target.
FAQ
Questions
Straight answers, including the ones that occasionally lose us work.
It depends on the problem, but we can usually tell you after reviewing a sample. For many structured business problems a few thousand well-labelled records is a workable start; for vision problems it is typically more.
That is normal and it is part of the work. Cleaning, labelling strategy and pipeline design are usually a larger share of the project than modelling.
Yes — error analysis, feature work, retraining and deployment improvements. We start by reproducing your current results so improvements are measured, not claimed.
Also relevant
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Web applications that stay fast on a mid-range phone and a mediocre connection, because that is what most of your users have.
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One codebase, two stores, and a release process your team can actually run after we hand it over.
Read moreTell us what you are trying to build.
Send the problem, the constraint or the half-formed idea. You will get a straight answer on whether we are the right team for it, and what it would take.