AI & Machine Learning
Most AI projects fail somewhere between the notebook and the load balancer. We build for the second half of that journey: the evaluation set, the monitoring, the fallback path when the model is wrong, and the retraining loop that keeps it from drifting.
A demo proves a model can be right once. Production asks whether it is right often enough, fast enough, and whether anyone will notice the day it stops being right.
Typically a two-week discovery, a six-week proof against real data, then build. Most AI engagements reach production between four and seven months.
AI & Machine Learning
Retrieval-augmented generation
Assistants grounded in your contracts, policies and manuals — with citations back to the source paragraph, chunking tuned for Arabic morphology, and a refusal path when the answer is not in the corpus.
Forecasting & decision models
Demand, load, churn, risk and capacity models with honest confidence intervals — plus the backtest that shows how they would have behaved over your last three years.
Computer vision
Defect detection, safety monitoring, document and plate recognition — trained on your imagery, deployed to the edge when latency or connectivity demands it.
Evaluation & MLOps
A scored test set that ships before the feature does, regression gates in CI, drift alarms, and a rollback that takes one command rather than one meeting.
What remains with you after we leave
Every item below is yours, in your repository and your cloud account, with no runtime licence and no lock to us.
- A labelled evaluation set owned by your team
- Model service with versioned endpoints and a documented API
- Monitoring dashboards for accuracy, latency, cost and drift
- Retraining pipeline with a human approval gate
- A written runbook covering the three most likely failure modes
Questions we hear a lot
Rarely, and only when the case is strong. Fine-tuning or retrieval on top of an existing foundation model is cheaper, faster and usually more accurate. We will tell you when it is not.
Yes. We deploy to in-Kingdom cloud regions, to a private tenancy, or fully on-premises on your own GPUs where regulation or policy requires it.
Grounding, citations, and a scored refusal rate treated as a first-class metric. A system that says "I do not know" at the right moment is worth more than one that is confidently wrong.
You may also need
Tell us what you are trying to build.
Send a short note and one of our engineers — not a salesperson — will reply within one business day.