AI solutions in Jordan succeed or fail on data readiness and deployment planning far more often than on model choice.
Most AI projects in Jordan never reach production. The model is rarely the reason — the data was not ready, nobody owned the outcome, or there was no plan for how the thing would actually run once it worked.
We build AI solutions in Jordan the other way round: deployment path first, data audit second, model last. That sequence is less exciting and considerably more likely to end with something running in your business.
What we build
Demand forecasting
Given eighteen months or more of clean transaction history, forecasting reliably beats manual planning. We have used it to cut stockouts in retail, size production runs in manufacturing, and staff clinics against predicted patient volume. The savings usually show up in inventory before they show up anywhere else.
Document intelligence
Extracting structured data from invoices, contracts, delivery notes and forms — including scanned Arabic documents, which most off-the-shelf tools handle badly. This has the clearest before-and-after of anything we do: a task that took a person an hour takes the system seconds.
Computer vision
Quality inspection on production lines, defect classification, and counting or measurement tasks that people do accurately at the start of a shift and less accurately at the end. The honest constraint is training data: if you have not been photographing your rejects, we start with a collection phase and say so up front.
Anomaly and fraud detection
Transaction scoring, unusual-pattern detection and equipment failure prediction. These suit machine learning well because the cost of a miss is quantifiable, which makes the business case straightforward to evaluate.
Language and chat interfaces
Support triage, response drafting and internal knowledge search. We build these with a human approving before anything reaches a customer — unsupervised models talking directly to your clients is a risk we do not recommend taking.
How we work
An engagement runs in four stages. Discovery establishes the single measurable outcome and confirms the data can support it. Data preparation is usually the largest phase and the one clients least expect. Model development is iterative, with you seeing results weekly rather than at the end. Deployment and handover includes monitoring, a retraining schedule and documentation your team can work from.
What it costs
A focused AI project typically runs from 6,000 JOD for a well-scoped single use case with clean data, to 25,000–40,000 JOD for something spanning multiple systems with significant data preparation. We quote after discovery, not before — anyone pricing an AI project without seeing your data is guessing.
