Machine Learning services
Models that earn their place in the business.
Machine learning is worth it when a rule cannot express the pattern and you have enough history to learn from. Forecasting demand, scoring risk, spotting the transaction that does not look like the others. Where a rule would do, we will tell you to write the rule.
Overview
Machine Learning, the way we do it
A model is a maintenance commitment, not a feature you ship once. Data drifts, behaviour changes, and a model that was accurate in March quietly stops being accurate by September unless someone is watching. Anyone selling you a model without telling you that is selling you a problem for later.
So we start with whether the data supports it at all: enough history, labelled honestly, representative of what you will actually see. Then the smallest model that does the job, measured against the plain baseline it has to beat, and monitored afterwards so you find out when it drifts.
What's included
What you actually get
- An honest read first on whether your data can support a model at all
- Measured against the simple baseline it has to beat, not against nothing
- The smallest model that does the job, because it is the one you can maintain
- Monitored after launch, so drift is noticed rather than discovered
- Explained in terms of what it changes for the business, not its architecture
How we work
Five steps, no surprises
- 01
We talk
A call or a WhatsApp thread. You tell us what is not working; we tell you honestly whether we are the right people.
- 02
We scope it
A written plan with what you get, what it costs and how long it takes. Fixed, so there are no surprises later.
- 03
We build it
You see it as it goes, not at the end. Changes are cheap while it is still being built.
- 04
We put it live
On infrastructure we set up and secure, tested before the launch date.
- 05
We keep it running
Updates, monitoring and someone who answers. Most clients stay on a monthly agreement.
Questions
About machine learning
How much data do we need?
It depends on the problem, and the honest answer is often "more than you have, labelled better than it is". We will look at what exists and tell you plainly whether it is enough before anyone commits to a project.
What happens when it stops working?
It will, eventually, because data drifts. That is why monitoring is part of the work rather than an extra: you find out from a dashboard rather than from a customer.
Has BitBee delivered a machine learning project?
Not among the work on this site. We list it because we build in it, and we would rather say that than let the page imply otherwise.
Also
Related work
- Quality AssuranceFind it before your customers do.
- UX DesignWork out what it should do before anyone builds it.
- UI DesignScreens people can use without being trained.
- Design SystemsOne set of rules, so the tenth screen looks like the first.
- Mobile ApplicationsApps people keep on the first screen.
- iOSiPhone apps built the way Apple expects.
- AndroidAndroid apps that work on the phones people actually own.
- FlutterFlutter, when one team has to cover both stores.
- AIAI that does one job, properly.
- Data ScienceAnswers from the data you already have.
- LLMsLanguage models, kept on a short leash.
- Generative AIGeneration with a human still holding the pen.
- PythonPython, for the work that has to be read as well as run.
- Back-EndWhere an order becomes an order.
- DatabaseThe part of your system that is hardest to fix later.
- Node.jsNode.js, for the systems that have to answer quickly.
- GoGo, where it has to be fast and stay simple.
- .NET.NET, for the systems a business runs on.
- JavaJava, for systems measured in decades.
- Front-EndThe half of your product people actually see.
- Web DevelopmentCompany sites, customer portals, and web systems that hold up.
- ReactReact, built so the next team can still work on it.
- AngularAngular, for systems that have to last.
Tell us what you have in mind.
An engineer reads your message and replies to you directly.
Talk to us