Software that holds everything together.

We design, build and run the systems businesses depend on. Web platforms, mobile apps, cloud infrastructure and applied AI, delivered by the engineers who will still be answering the phone next year.

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01 Who we are

A studio grows out of the work it keeps being asked to do. Ours grew out of one question, asked over and over by businesses in Abu Dhabi. Can you make this actually work?

Not a prototype, not a slide, not a plan for next quarter. Something real, in production, that staff can use on a Monday morning and that keeps running long after the launch is forgotten.

/ 01

We start with the business

Before any architecture is drawn we sit with the people who will use the thing. What breaks today, what it costs when it breaks, and who carries that cost. Most of what looks like a software problem turns out to be a process nobody has written down.

/ 02

We build in the open

Work goes to a live environment every week from the first week. You click it, you break it, you tell us where we misunderstood. Nothing disappears into a long silent build phase and arrives wrong at the end, because there is no long silent build phase.

/ 03

We stay after launch

Launch is the beginning of the part that matters. We monitor, patch, tune and extend, and when you would rather run it yourself we hand over documentation a new engineer can actually follow. Everything is in your name from day one.

6 Disciplines

AI, ML, computer science, web, DevOps and Linux under one roof.

1 Team, start to finish

The engineers in the first call are the engineers on the commits.

7 Day release cycle

Something real reaches a live environment every week.

24/7 Reachable

Call, email or WhatsApp. A named engineer answers.

02 What we do

Deep in the machine.

Six practices, explained properly rather than listed. Each figure is live, drawn from the same idea it describes, and the work underneath each one has already shipped.

Natural language processing

Teaching software to read.

Text arrives messy. Arabic and English in one sentence, dates written five ways, a product name spelled differently by every branch. Before a model can be useful, that has to become structure.

Tokenisation and normalisation tuned for mixed Arabic and Latin script, where most defaults quietly fail
Entity extraction that pulls names, amounts, dates and references out of free text and into your schema
Classification and routing, so a message reaches the right desk without a person reading it first
Sentiment and intent scoring measured against a labelled set you own, not a vendor benchmark

TokenisationEntitiesClassificationArabic NLP

Tokens resolving into structure

Retrieval augmented generation

Answers with a source attached.

A language model on its own will invent an answer rather than admit it does not know. Retrieval fixes that. The question goes to your own documents first, the closest passages come back, and the model may only answer from what it was handed.

Your corpus indexed and chunked, with the chunk size tuned against real questions rather than guessed
Reranking, so the three passages that actually matter beat the twenty that merely look similar
Every answer carries citations back to the source paragraph, which is what makes it auditable
An evaluation set that gates deploys, so a prompt change cannot quietly make accuracy worse
Runs inside your own network when the documents cannot leave it

Vector searchRerankingCitationsEvals

Query out, passages back, answer cited

Machine learning

Models judged on the cases you lose.

Accuracy is the number everyone quotes and the one that hides the problem. What matters is the shape of the mistakes. A fraud model at ninety nine percent that misses the one real case has failed, and only a confusion matrix will tell you that.

Forecasting, classification, anomaly detection and recommendation, sized to the data you actually hold
Precision and recall traded deliberately against what a false positive and a false negative each cost you
Training pipelines that reproduce, with versioned data and pinned environments
Drift monitoring, because a model that was right last year is not automatically right today

PyTorchScikit learnFeature storesDrift

Confusion matrix, diagonal running hot

Shipped / reporting and analysis

Reporting dashboard with a weekly comparison chart

Web apps Statistical analysis

Corporate site on a handset with a language switcher

Web apps Responsive

Model training and evaluation

Convergence you can watch.

Training is not a black box you start on Friday and hope about on Monday. Loss is plotted, runs are versioned, and every experiment is recorded with the data and the settings that produced it, so a result can be reproduced months later by somebody else.

Baselines first, because a simple model that works beats a complex one nobody can explain
Fine tuning on your own data when a general model is close but not close enough
Cost and latency budgeted per request and enforced in the build, not discovered on the invoice
Held out test sets kept honest, so the numbers you are shown are the numbers you will get

Fine tuningMLflowBenchmarksReproducible runs

Loss descending across epochs

Computer vision

Reading what the camera sees.

Plates at a gate, stock on a shelf, damage on a vehicle, a signature on a delivery note. Vision earns its place when it removes a person from a loop they resent being in, and it has to work in the light and the dust you actually have.

Detection and recognition tuned on images from your site, not a public dataset from another country
Text extraction from documents and photographs, including Arabic script
Inference at the edge when a round trip to the cloud is too slow or the connection is unreliable
A confidence threshold with a human review path, because being confidently wrong is expensive

DetectionOCREdge inferenceONNX

Feature sweep with detections locking on

Shipped / interfaces on real devices

Mobile commerce cart with digital products priced in dirhams

Mobile apps Interface design

Auction listing screen with image carousel and bid controls

Mobile apps Carousels

Arabic menu screen with a nutrition breakdown

Mobile apps Localisation

Checkout screen showing saved payment methods

Mobile apps Payment gateways

Platforms and delivery

The part that keeps it alive.

Everything above only matters if it reaches production and stays there. Environments rebuild from an empty account, deploys roll back on their own when a health check fails, and the alerts that wake somebody are the ones that deserve to.

Web platforms and mobile apps across Laravel, .NET, Django, React, Vue and Flutter
Infrastructure written down as code on Azure, AWS or hardware you own, in the region residency requires
Automated build, test, scan and release, so shipping is boring and therefore frequent
Backups with a restore that has actually been tested, because an untested backup is only a rumour

LinuxDockerKubernetesTerraformCI/CD

Build, test, scan, stage, ship

Shipped / released to both stores

Mobile apps Animated splash

Account and authentication screen in a dark theme

Mobile apps Authentication

04 Trusted by

Businesses in commerce, property, exploration and hospitality.

05 Stack

Chosen per problem, not per fashion.

This is what we reach for most. If you already have a team and a stack we will work in yours instead.

Microsoft

Azure

AWS

Linux

Ubuntu

Windows

Docker

Kubernetes

Terraform

.NET

Laravel

Django

PHP

Python

Node

JavaScript

React

Vue

Flutter

Swift

Android

Apple

HTML

CSS

Sass

Vite

Webpack

Postgres

MySQL

MariaDB

MongoDB

GitHub

Git

Figma

npm

READY WHEN YOU ARE

Tell us what needs to exist.

Send the messy version. A paragraph about the problem is worth more than a polished specification, and we will come back with questions inside one working day.

Telephone +971-55-5097590

Email hello@atherium.dev

WhatsApp Message us

Studio Hamdan Street
Abu Dhabi, United Arab Emirates

Hours Open 24/7
Abu Dhabi, Gulf Standard Time, UTC+4

Your name

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What can we help with

What needs to exist

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