Example chart of weekly demand. A solid history line runs through weeks one to four, then a dashed forecast continues through weeks five to seven inside a widening band of uncertainty. A reorder point line is marked on the chart, and the week the forecast crosses it, week six, carries the note “order placed here”.
Crosses the reorder point, the order goes in on its own
Predictive analytics that reaches production
We build predictive analytics and machine-learning models for businesses in Cyprus and across EMEA, wired into the decisions you make every day. Not a notebook that demos once and dies.
We build predictive analytics and machine-learning models for businesses in Cyprus and across EMEA. The difference is where they end up. Most models demo well, impress a room, then sit in a report nobody acts on. We build ours to run in production, wired into the decision they were meant to change.
A prediction is worth making only if it moves something you do. We start there, and we stay honest about it the whole way through.
Problems, not algorithms
You do not have a clustering problem. You have a stockroom that is either empty or overflowing, customers leaving before you notice, or a machine that fails the week after you needed it running. We start from the problem you can name.
- Demand
Demand and sales forecasting
Plan inventory and staffing against what is coming, not last year's guess.
- Churn
Churn and retention
See which customers are about to leave while you can still keep them.
- Risk
Risk and fraud
Flag the transactions and accounts that need a human's eyes, and let the rest through.
- Maintenance
Predictive maintenance
Fix the machine before it breaks, not after it takes the line down.
- Pricing
Pricing and optimisation
Price against the patterns already sitting in your own data.
We will tell you when not to build a model
Most pages in this category sell you a model before they have asked whether you need one. We disqualify bad fits, because a model you do not use is worse than no model at all.
Go or no-go worksheet with four build criteria. A decision changes if the forecast improves, checked. Enough clean history to learn the pattern, left unchecked, thin, most of a year is missing. No simpler rule already covers it, checked. Someone owns monitoring after launch, checked. Verdict: three clear, one thin, we fix the data first and say so before we build anything.
Only if it changes a decision
A model earns its build only if a better prediction changes a decision you will actually make. If it will not, we will say so.
Only with enough clean history
ML needs enough clean, relevant history. If the data is not there yet, the honest first project is fixing the data, not training on noise.
Sometimes a rule is the answer
Sometimes the right answer is a simple rule or a dashboard, not machine learning. We will tell you that rather than sell you a model.
Built to be monitored
When ML is the right call, we build it to be monitored and retrained. A model accurate at launch and ignored for a year is a liability, not an asset.
From data to a decision, in four steps
The same path runs every engagement. It fails fast and cheap when the data will not support the goal, and ships clean when it will.
- 01
Audit
We look at the decision, the data behind it, and whether a model can move it. An honest go or no-go before you commit to a build.
- 02
Design
We agree the target, the success metric, and exactly how the prediction reaches the person or system that acts on it.
- 03
Build
We build, validate against held-out data, and prove it on your real history before it touches a live decision.
- 04
Hand over
Deployed where the decision happens, with monitoring and a retraining plan. It keeps working after we are gone.
A prediction in a report changes nothing
The gap most projects fall into is the last metre: a model that is accurate and unused. We close it. We connect the model to the workflow that acts on it, the alert, the queue, the dashboard, the automated step, so the prediction becomes a decision someone makes or a step that runs on its own.
This is also the operational discipline of keeping a model alive: deployed, monitored, versioned, and retrained as the world drifts. Accuracy on launch day is the easy part. Accuracy a year later is the work.
Often the highest-value build is both halves at once: predict, then act. That is where predictive analytics meets automation.
Forecasts we stake our own books on.
Predictive analytics is one specialism within how we work. The proof we point to is our own product, Pileform, in daily production use across 55 VAT jurisdictions and 11 languages. We build what we recommend, and hold your models to the same production standard.
Questions, answered plainly
What is predictive analytics, in plain terms?
It is using the patterns in your past data to estimate what is likely to happen next: which customers will churn, how much stock you will need, which transactions look risky. The point is to act before it happens instead of after.
How much data do we need?
Enough clean, relevant history for the pattern to be learnable, which varies by problem. If you do not have it yet, the honest first step is fixing the data, and we will say so rather than build a weak model on thin ground.
Will the model actually get used, or just sit in a report?
That is the whole point of how we work. We connect the prediction to the workflow that acts on it, an alert, a queue, an automated step, so it changes a real decision. A model nobody uses is a failure, and we treat it as one.
How accurate will it be?
We validate against your real, held-out history and report honest performance before anything goes live. We will not promise a number we cannot prove on your data.
What happens when the world changes and the model drifts?
Models decay. We hand over monitoring and a retraining plan so accuracy is tracked and the model stays useful, not just accurate on the day it launched.
Is this different from AI consulting?
It is a specialism within it. Predictive analytics is one of the things we build. If you are earlier and unsure where AI fits at all, start with our AI consulting page.
Can predictive analytics work alongside automation?
Yes, and it is often the most valuable combination: predict, then act automatically. See process automation.
Tell us the decision you wish you could see coming. We will tell you honestly whether your data can get you there, and what it would take to put the answer in front of the person who acts on it. .