Services · Artificial intelligence · Machine learning development

Predictions from your own data, explained in plain terms.

Our machine learning development turns the data you already hold into models that forecast demand, flag customers likely to leave, score leads or risk, recommend products and spot unusual activity. Each model is trained on your history and tested on data it has not seen.
We explain what drives each prediction, so the people who act on it understand why the model said what it said.
Get in touchAll AI services
Built for
Finance, sales, operations and product leaders with a few years of transaction, customer or operational data.
Typical timeline
A first model usually takes eight to twelve weeks, most of it spent understanding and preparing the data.
What you own
You own the trained models, training code, feature pipelines and documentation, stored in your own environment.
Rated
4.7 / 5 from 23 Google reviews

200+ companies across the US and the UAEFrom a first store to a listed developer

Modon
Majid Al Futtaim
HP
Belkin
Dulsco Group
OneClickDrive
Hanayen
HMS
Clocksprings
Ghezlan

What you get

Models we build

Classical machine learning still answers many business questions better than a language model. These are the most common.
Explain your project

  • Demand forecasting

    We forecast sales, stock needs or staffing by product, branch or region, taking account of seasons, holidays and promotions. Forecasts come with a range, not a single number.

    Planners can order and staff against a realistic range rather than a guess.

  • Churn prediction

    A model scores each customer on how likely they are to leave, using usage, payment and support history. The scores feed into your CRM.

    Retention teams can reach the right customers before they leave.

  • Lead and risk scoring

    We score leads on likelihood to convert, or applications and transactions on risk, using your past outcomes. Thresholds are set with your team.

    Staff spend their time on the cases most worth their attention.

  • Recommendation

    Recommendation models suggest products, content or next actions based on what similar customers did. We test them against simple baselines such as best sellers.

    Customers see more relevant items and you can see whether the model beats the baseline.

  • Anomaly detection

    Models learn normal patterns in transactions, sensor readings or system logs and flag what does not fit. Alerts go to a person to investigate.

    Unusual activity is spotted early instead of at month end.

  • Data preparation

    We find, clean and join the data, deal with gaps and duplicates and build features that a model can learn from. This step is documented so it can be repeated.

    Your data becomes usable for this model and the next one.

  • Explainability

    For each prediction we show the factors that pushed it up or down, using methods such as SHAP. Summaries are written for business users, not data scientists.

    Decision makers can question a prediction instead of trusting it blindly.

  • Model delivery

    Models are delivered as an API, a scheduled batch job or scores written back to your database or BI tool. We build with Python, scikit-learn, PyTorch and MLflow.

    Predictions arrive where your team already works.

Who this is for

Is your problem a machine learning problem

Machine learning works when you have history, a clear outcome to predict and a decision that changes because of the prediction.
01

You plan stock or staff on gut feel

A forecast trained on your sales history gives planners a number and a range to work from, updated every week.

Needs at least two years of history.

02

You lose customers without warning

Churn scores show which accounts are at risk and why, so retention work is aimed at the right people.

Scores can feed straight into your CRM.

03

Fraud or errors are found too late

Anomaly detection flags unusual transactions or readings as they happen, for a person to review.

Alerts are tuned with your team.

How a model gets built

  1. 01FrameWe define the question, the decision it supports and how success will be measured.
  2. 02Prepare dataWe audit, clean and join your data and agree what can and cannot be used.
  3. 03Train and testWe train several models, compare them with a simple baseline and test on held back data.
  4. 04DeployWe deliver the model where your team will use it and set up monitoring for drift.

Technology

What we build this with.

Models are chosen per use case for quality, cost and where your data is allowed to go. The systems around them run on the stack below.

01Web

  • React
  • Next.js
  • TypeScript
  • Node.js
  • Laravel
  • WordPress

02Data and cloud

  • PostgreSQL
  • MySQL
  • MongoDB
  • AWS
  • Azure
  • Docker

Questions about machine learning

Enough history to show the pattern.

It depends on the problem. Forecasting usually needs two or more years so seasons repeat. Churn and scoring need enough past examples of the outcome. A short data review tells us whether what you have is enough.

Services that usually go with this one.

MLOps and AI platformsKeep your models working after launch day.Computer vision developmentCameras and images that report what they see.AI consulting and strategyKnow which AI projects to fund before you fund them.

Tell us what the business needs. We will tell you what to build first.

Schedule a free consultation and get a written recommendation, with costs, before you commit to anything.

Get in touch

PROVEN RESULTS

4.7 / 5from 23 Google reviews