NXAARA Next-generation AI Architecture, Resources and Acceleration
Build, fine-tune and deploy AI on NXAARA.
GPU compute, dataset preparation, model tuning, retrieval and production inference — in one platform, metered by what you use, operated from the United Arab Emirates.
- Usage-based billing
- Console, CLI and REST API
- Shared, dedicated or on-premises
- Built for Arabic and multilingual work
Built and operated by EADPAG ↗
Platform services
Everything between raw data and a working product.
Use one service or the whole stack. They share the same projects, permissions, billing and audit log, so nothing has to be stitched together afterwards.
GPU Cloud
Launch single GPUs or multi-node clusters for training, tuning and high-throughput inference. Per-second metering, no minimum term.
Explore compute →Model Foundry
Tune an open base model on your data, score it against a held-out set, compare it to what is already serving, and publish only if it wins.
Build a model →Synthetic Data Studio
Generate text, tabular and document data when the real thing is sensitive or scarce — with privacy and utility checks before you train on it.
Generate data →Inference Cloud
Deploy models as serverless, dedicated or private endpoints. OpenAI-compatible API, streaming, autoscaling and per-token metering.
Deploy an endpoint →Knowledge Cloud
Managed ingestion, Arabic and English OCR, hybrid search, reranking and answers that cite the document and page they came from.
Build a knowledge base →Private AI
The same platform on dedicated hardware, in your own data centre, or fully air-gapped — with identical APIs and workflows.
See private options →NXAARA Console
See what is running, what it costs, and who changed it.
Create projects, provision GPUs, register datasets, launch tuning jobs and monitor endpoints from one interface — with every action written to an audit log you can export.
Illustrative preview — not live data
Workflow
From an idea to something in production.
A real sequence rather than a feature list — each step depends on the one before it.
Create a project
A workspace with its own members, budget, network boundary and keys.
Bring your data
Upload, connect object storage, or generate a checked synthetic set.
Run the workload
GPU jobs, a tuning run, or a retrieval index — whichever the problem needs.
Prove it works
Score against a held-out set. Compare quality, latency and cost against the incumbent.
Deploy it
Publish to an endpoint, or install the whole platform inside your own boundary.
- Your data stays yours — we do not train on it, pool it, or sample it
- Residency you can put in a contract — UAE region, operated by EADPAG
- Evidence, not assertions — evaluation results and audit trails you can export
- No lock-in by design — export your weights, use an OpenAI-compatible API, leave if you need to
- One boundary to the private edition — the same platform runs air-gapped
- Arabic treated as a first language — OCR, generation and evaluation, not a translation layer
Why NXAARA
Built by a team that has to live with the audit trail.
EADPAG's other work includes regulated clinical AI, where a system is not permitted to claim it was careful — it has to be able to demonstrate what it did, on what evidence, and who approved it.
That discipline is why versions here carry lineage, why promotion to production requires an explicit approval, and why we would rather publish a capability we can defend than a number we cannot.
FAQ
Questions we get asked first
What is NXAARA?
NXAARA is an AI cloud platform operated from the United Arab Emirates by EADPAG. It provides GPU compute, model fine-tuning, synthetic data generation, retrieval and production inference through one console, one API and one billing relationship.
How is this different from renting GPUs somewhere cheaper?
Raw GPU rental is one line item in a much longer list. The work that consumes a team's time is dataset preparation, evaluation, deployment, access control and cost attribution. NXAARA provides those as connected services rather than as five separate vendors you integrate yourself. If all you need is a bare GPU by the hour, a commodity provider may well be the better answer, and we will say so.
Do you train on customer data?
No. Customer datasets are used inside the customer's own project, for that customer's own models, and for nothing else. We do not pool data across accounts and we do not use it to improve base models.
Is NXAARA suitable for regulated workloads?
It is designed for them, with residency, isolation, audit logging and private deployment options. Whether it satisfies a specific obligation in your sector is a decision for your compliance function, and we will supply the technical evidence they need to make it rather than claiming the answer on their behalf.
Can I use NXAARA for Arabic-language workloads?
Yes, and it is a deliberate area of focus. Arabic OCR is part of the retrieval pipeline, Arabic and Gulf-dialect generation is supported in Synthetic Data Studio, and Model Foundry is regularly used to improve Arabic performance in open base models.
Build your next AI product on NXAARA.
Create a project, choose your infrastructure, and move from experiment to production without changing platforms halfway.