Applied ML engineering
Not every problem wants a language model. Forecasting demand, scoring risk, classifying documents, ranking a queue, extracting fields from a form — these are long-solved problems with well-understood methods that are cheaper, faster, and easier to defend than a prompt.
The model that works in the notebook and nowhere else
A trained model is perhaps a fifth of the job. The rest is serving it reliably, feeding it the same features in production that it saw in training, monitoring for the drift that quietly degrades it, and retraining without breaking anything downstream. Teams that skip that part end up with an impressive result that never reaches a user.
You’ll recognise this if
- A data scientist built something promising that never shipped
- Forecasts live in a spreadsheet that one person maintains by hand
- Staff manually sort, tag, or route high volumes of similar items
- A model went live once and nobody has checked it since
What we actually deliver
A model matched to the problem
We start simple and stay there when simple wins. A well-built gradient boosting model frequently beats something far more elaborate, costs a fraction to run, and can be explained to a regulator.
Serving that fits your architecture
Batch, real-time API, or embedded in an existing job — whichever your systems and latency budget actually call for, deployed properly rather than left on someone's machine.
Monitoring for drift and degradation
The world moves and quietly invalidates a model. We instrument for that so it surfaces as an alert rather than as a quarter of bad decisions nobody traced back.
A retraining path your team can run
A documented, repeatable route to retrain and redeploy — so the model stays alive after we leave rather than decaying into a system nobody dares touch.
The way we approach it
Agree the metric before modelling
What counts as good, what a false positive costs relative to a false negative, and what the current process achieves. Without a baseline there is no way to say whether the model is an improvement.
Take a weak model to production early
An honest end-to-end pipeline with a mediocre model beats an excellent model with no path to a user. Once the pipeline is real, improving the model is straightforward and measurable.
Explainability where it's required
In regulated or high-impact decisions, being able to say why a prediction came out that way is a requirement, not a nicety. That constrains model choice, and we make the tradeoff deliberately and up front.
What changes
- A model serving real traffic, not sitting in a notebook
- A measured comparison against whatever the process does today
- Alerting when performance degrades, before it shows up in results
- A retraining procedure your own team can execute
Asked often enough to answer here
How much data do we need?
It depends far more on the problem than on a headline number. Some classification tasks work from a few thousand labelled examples; forecasting typically wants a few years of history to capture seasonality. We assess feasibility against your actual data before committing to an approach.
Should we use a language model for this instead?
Sometimes, and we will say so. Language models are excellent for unstructured text and genuinely novel tasks. For structured, high-volume, repetitive prediction they are usually slower, more expensive, and harder to defend than a conventional model.
Can you work with the model our team already built?
Often that is the most efficient route. Taking an existing model from notebook to production — feature pipeline, serving, monitoring, retraining — is a well-defined engagement and avoids throwing away good work.
Related
Wherever you’re starting from, let’s figure out the next step.
Tell us what you’re building — we’ll tell you honestly whether we’re the right team for it.