Cost and price prediction
Regression models that estimate a cost or price from project parameters, with a confidence range, such as our construction cost predictor trained on 118,950+ Dubai permits.
What we build
Machine Learning & MLOps services
Deal and lead scoring
Rank listings, leads or opportunities so your team works the best ones first, like the A+ to F grading in our UAE property deal finder.
Demand and time-series forecasting
Forecast volumes, bookings or stock with models that account for seasonality, holidays and the Ramadan effect.
Churn and customer models
Find which customers are likely to leave or upgrade, and what drives it, so retention and sales can act on it.
MLOps pipelines
Versioned data and models, reproducible training, automated tests and one-command deployment behind an API.
Monitoring and retraining
Track input drift and accuracy in production, alert when performance slips, and retrain on fresh data on a schedule or trigger.
Start with the decision, not the algorithm
A model is only useful if someone changes a decision because of it. We start every project by agreeing which decision the model will support, who acts on it, and what a good outcome looks like in numbers. That sets the metric we optimise and the threshold the model has to beat, usually the method you use today.
We then audit the data you have. Many projects in the UAE run on public or semi-public datasets plus a company's own records. Our Dubai construction cost predictor, for example, is trained on 118,950+ real building permits across 10 districts with a reported 99.7% R².
How a project runs
- Discovery and data audit, 1 to 2 weeks. The decision, the metric, the data you have and the gaps.
- Baseline and first model, 2 to 4 weeks. A simple baseline first, then gradient boosting or deep learning only where it clearly wins.
- Deployment. An API or a dashboard your team actually opens, wired into your existing systems.
- MLOps. Monitoring for drift and accuracy, alerting, and a retraining pipeline so performance holds as your data changes.
Explainable by default
Business users trust a number they can question. Every model we ship comes with feature importance and per-prediction explanations, so a sales manager can see why a deal scored an A and an estimator can see which inputs drove a cost. Where regulation or audit matters, we document the training data, the evaluation and the limits of the model in plain language.
Pricing and timelines
Custom machine learning builds are scoped after a discovery call and usually start from AED 35,000. Discovery and a baseline model can be run as a smaller fixed-price first phase. Every engagement starts with a written scope, a fixed first step and a delivery date.
Technology we use
Questions
How much data do we need?
It depends on the problem. Scoring and cost models can work with a few thousand good historical records; forecasting needs at least one to two years of history. The data audit in week one gives you a clear answer before you commit to a build.
Can you use public UAE data?
Yes, where it is licensed for the use. Our construction cost predictor combines public Dubai permit data with modelling, and our property deal finder works from public listing data. Licensing of each source is checked during the data audit.
What happens when the model gets worse over time?
We monitor input drift and accuracy in production and alert when they move. A retraining pipeline retrains on fresh data and only promotes the new model if it beats the current one on a held-out test.
Who owns the model?
You do: the code, the trained model and the data pipeline. We deploy on your infrastructure or a UAE-hosted cloud you control.
How much does it cost?
Custom builds usually start from AED 35,000 and are scoped after discovery. A fixed-price discovery and baseline phase is available if you want to test feasibility first.
Other services: Computer Vision Development · Generative AI & LLM Applications · AI Visibility (GEO)
Planning a machine learning project?
Tell us the problem and the data or cameras you have. We reply within one business day with a first step, a price and a date.