Machine Learning & MLOps
Predict what's next, and trust the number, week after week.
Forecasting demand, spotting customers about to leave, catching fraud: machine learning turns your history into better decisions. We build the simplest model that does the job, put it into your systems and keep it accurate as things change.
From the first question, “do we even need machine learning?”, to a model that runs reliably in your systems.
What we build
Problem framing
We check whether you need machine learning at all, or whether a rule or a report would do, before you spend on a build.
Custom models
Forecasting, scoring, classification, and image or document models, chosen for the job rather than the trend.
Built into your systems
The APIs, data pipelines and screens that put predictions where decisions get made.
Safe releases (MLOps)
Repeatable training, versioned models and staged rollouts, so a new model never quietly breaks the old one.
Monitoring and retraining
We watch accuracy, cost and changes in your data, alert when quality slips and retrain when it's needed.
Where it pays off
- Forecasting. Demand, stock, staffing and revenue predictions that beat the spreadsheet.
- Churn and conversion. Score which customers might leave or buy, so your team acts on the right ones.
- Documents and images. Read scans, forms and photos that used to need a person.
- Fraud and anomalies. Catch rare, costly events that fixed rules miss.
- Recommendations. Show each customer what they're most likely to want next.
How a model project runs
You get an honest go / no-go on your data before you pay for a build.
- Check the data. We test whether your data can support a model and agree how success will be measured.
- Pilot a first model. A fixed-price first model, measured against that goal.
- Put it to work. We connect it to your systems with testing, versioning and monitoring.
- Run and retrain. We watch it live and retrain when accuracy slips, or hand it over to your team.
Sometimes the most valuable advice is not to use machine learning at all. If a simple rule does the job, you'll get the rule.
Recent work
Questions, answered
Anything else? Ask us directly. We reply to every message personally.
Do we have enough data?
Often more than you think, but not always. The first step is a data check that tells you honestly whether a model is feasible before you pay for a build.
We already have data scientists. Can you help?
Yes. We often take models your team has built and put them into production with proper releases and monitoring, or set up the pipeline so your team can ship on its own.
What is MLOps?
The practices that keep models reliable once they're live: repeatable training, version control for models, safe rollouts, monitoring and retraining.
Where will the model run?
In your own cloud account or wherever suits your systems. Predictions can be instant, through an API, or run in batches overnight.
What happens when the model gets worse over time?
We monitor accuracy and the data coming in, alert when either drifts, and retrain the model or roll back to an earlier version.
Should we hire our own ML team instead?
If machine learning is core to your product and you can hire senior people, yes, and we can help you get there. If it matters but isn't core, it's usually cheaper to have us build and run it.
Tell us what you want AI to do.
Someone senior reads every message and replies personally. Short notes are fine; we'll ask the right questions on the call.