MLOps, AI Deployment & Monitoring · UAE & Middle East
From prototype to production — and kept healthy there
Most AI projects stall between the demo and production. Wrexa builds the pipelines, infrastructure, evaluation and monitoring that make AI reliable, secure and affordable at scale.
remote-first — we work with teams across the UAE and the wider Middle East
- A working prototype can't handle real traffic or real data
- Nobody knows when model quality drops
- AI infrastructure and API bills keep climbing
- Security and compliance teams need controls before launch
What we build
MLOps & deployment: what you get
Cloud AI deployment
Production infrastructure on AWS, Azure, GCP, private cloud or edge, defined as code.
CI/CD for models & prompts
Versioned models, prompts and datasets with automated tests and rollbacks.
AI evaluation
Accuracy, latency, cost and safety tests run on every change.
Monitoring & observability
Tracing, drift detection, quality dashboards and alerting.
Cost & latency optimisation
Caching, routing to smaller models, batching and right-sized infrastructure.
Use cases
Where it pays off
- SaaS
- Scaling AI features for thousands of users.
- Enterprise
- Private-cloud deployments with access controls and audit trails.
- Manufacturing
- Fleet management for edge vision models.
- Any team
- Rescuing a stalled AI prototype and making it production-ready.
How we build
From napkin sketch to production
01Discover
Business problem, AI feasibility, architecture.
02Data
Collection, cleaning, annotation, pipelines.
03Build
Models, agents, retrieval and the application.
04Evaluate
Accuracy, latency, cost and safety.
05Deploy
AWS, Azure, GCP, private cloud or edge.
06Operate
Monitoring, observability and optimisation.
Typical stack
FAQ
MLOps & deployment questions
Can you take over an AI system another team built?
Yes. We start with a technical review, then stabilise, add evaluation and monitoring, and improve from there.
Which cloud do you recommend?
The one your organisation already uses, in most cases. We deploy on AWS, Azure, GCP, private cloud and edge.
How do you reduce AI running costs?
By measuring first, then caching, routing simpler requests to smaller models, trimming context and right-sizing infrastructure.
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Serving clients across United Arab Emirates and Abu Dhabi and Sharjah and Saudi Arabia and Qatar and Oman and Bahrain and Kuwait and the United States and India.
your idea is the first frame —
Let's build your mlops & deployment project.
Share the problem, your data and your timeline. We'll reply with a practical path from idea to production — wherever you are in the UAE or the wider Middle East.