Working With Us Across Time Zones on AI Projects
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Time zones matter more on some AI projects than others
Clients reasonably ask whether working with a team several hours ahead will slow things down. For most software, our general arrangements are covered in how we work across time zones. Machine learning and AI projects have their own rhythm, though, and it changes the answer in both directions.
Some parts of AI work need you in real time: reviewing a batch of predictions with a domain expert, deciding what counts as a correct label, agreeing the go-live threshold. Other parts are long, quiet stretches of computation where nobody needs to talk at all. The trick is arranging the project so the first kind lands in the overlap and the second kind fills the rest.
Where our people are, and the overlap that follows
We have a UK head office in London and an engineering office in Lahore, Pakistan, several hours ahead of the UK. The overlap we can commit to varies by where you are, and we write it into the proposal rather than promising general flexibility.
| Your region | Typical overlap | How we run it |
|---|---|---|
| UK and Ireland | Most of the working day | Standard; live sessions any time in the UK morning or early afternoon |
| Western Europe | Most of the working day | Standard |
| Gulf and wider Middle East | Generous, close to a full day | Standard; the time difference is small |
| US East Coast | A few hours | Part of the team shifts later; live sessions in your morning |
| US West Coast | Short | Shifted hours and more asynchronous work |
The upside: experiments run while you sleep
Much of the middle of an ML project is iterative. Train a candidate model, evaluate it, adjust the features, try again. For UK and European clients, the engineering day starts before theirs. Questions raised in the afternoon often have an experiment run against them by the next morning, with results written up before the client opens their laptop.
- Overnight training runs and evaluation jobs, summarised by your morning
- Data audit findings posted early, ready for your domain expert's first coffee
- Shadow-mode comparisons refreshed daily, before anyone needs them
- Retraining and deployment windows outside your busiest trading hours
None of this requires anyone to work unreasonable hours. It is simply what happens when the calendar is used sensibly.
The downside, stated plainly
The weakness of any split-hours arrangement is a question that sits unanswered for a day and then gets answered ambiguously, costing a second day. On AI projects the most expensive version is a labelling question: is this case a late payer or not, is this image a defect or a scratch? If an engineer guesses and labels a thousand examples the wrong way, the model learns the wrong thing.
For US West Coast clients in particular, the overlap is short enough that this is a real risk. We do not pretend otherwise. We handle it with the practices below, and for some projects we recommend a short run of early sessions in the first two weeks, while the definitions are being settled, before relaxing into a mostly written rhythm.
The cost of a time difference is never the hours themselves. It is the decision that waits in silence.
How we keep AI decisions from waiting
- Written first. Twice-weekly updates in the shared channel: what moved, what is blocked, what we need from you and by when.
- Batched questions. Labelling and definition questions grouped into one document with examples, sent before your working day.
- Asynchronous reviews. Predictions to review arrive as a simple sheet with a column for your verdict, not a meeting request.
- Recorded demos. Every sprint demo recorded with a written summary, so attendance is useful but not required.
- Deadlines on decisions. Each request says the date after which it delays the work, so nothing waits quietly.
After launch: alerts and support across the day
Time zones matter again once a model is live, because an alert does not wait for office hours. Monitoring alerts go to a named person on our side and, where you want it, to someone on yours. For UK and European clients the engineering day covers the early morning, when overnight batch jobs have just finished and a failed scoring run is most likely to be noticed, so problems are often fixed before the people using the predictions sit down.
For US clients a failed overnight run in UK terms is an evening problem in theirs, which suits them well. What we agree in advance is who is alerted for what, how quickly a critical failure is acknowledged, and what the fallback is while it is being fixed. Every model we deploy has one, so a missed scoring run means yesterday's list or the old rule, not a blank screen.
The sessions worth being live for
A few moments on an AI project are much better live, and we schedule them firmly into the overlap from the start.
- The discovery workshop, where the decision and the data are explored together
- The first review of model predictions with your domain expert
- The proof of value results and the go or stop conversation
- The go-live decision and the review of the monitoring thresholds
- The handover sessions for whoever will own the model
Everything else can run asynchronously without harm. That is how SpiderHunts runs projects for clients in the UK, Europe, the Gulf and the US, and it applies equally to machine learning and AI integration work.
Frequently asked questions
What hours does the SpiderHunts team work?
Can we work with you from the United States?
Do we need to attend meetings outside our working hours?
How quickly do you respond to questions?
Worried the hours will not line up?
Tell us where your team is and when they work. We will tell you the overlap we can commit to, and how we would run the project around it.