What we build
From the model to the product it ships inside
Nine services, three groups. Most projects touch more than one.
Intelligence
Getting a model to do something useful with your data.
- AI Automation
- AI Modeling
- AI Chatbots & Systems
Systems
The engineering that makes it run the same way every time.
- Pipelines
- Systems & Engines
- Software Process
Product
What the people you are building for actually touch.
- Websites
- Arts & Graphic Design
- Consulting
Selected work
Things we have actually shipped
Engines, models and platforms — shipped for competitions, clients and ourselves.
ChaosX Engine
In development
A C++ game engine built from the ground up — custom application and entry-point architecture, event system, and logging, targeting Windows and Linux.
- C++
- premake5
- Cross-platform
GravitySort
Private4.6x cub::DeviceRadixSort on low-cardinality data, parity on uniform · N=10⁹, A100-80GB
A distribution sort for int32 keys, CPU and GPU. A gateway samples the input and routes it to a pipeline chosen for its distribution — counting and filling when values repeat, exiting after one pass when data already arrives ordered — so each route skips the work it does not need.
- CUDA
- A100
- Research
Energy Degradation Prediction
1st place — IEEE Jordan AI Modeling Hackathon 2.0 · RMSLE 0.19877
Predicting an energy degradation index across 42 channels under train/test distributional shift, blending four tabular foundation models.
- TabPFN
- Nori-30M
- TabFM
Chest X-Ray Bench
Best ensemble 0.9174 AUROC on test500
A controlled comparison of 43 chest X-ray models on CheXpert, separating the design choices that actually move scores from the ones that are just noise.
- CheXpert
- ConvNeXt
- Medical imaging
Sign Language Bridge
Fine-tuning Qwen3-VL-2B-Instruct for continuous ASL to English translation, with multi-tier LoRA, staged OpenASL and How2Sign training, and a MediaPipe preprocessing pipeline.
- Qwen3-VL
- LoRA
- MediaPipe
Health-Navigator
An agentic medical assistant that reasons over patient data through multi-agent orchestration, combining pre-trained ML models with relational and vector databases.
- Multi-agent
- Vector DB
- Jupyter
Team-Finder
Security-hardened team-matching platform that pairs students by skill similarity and verifies members against university email domains.
- Next.js
- FastAPI
- Supabase
Video Captioning
Samples frames with ffmpeg and sends them to vision models for captions in four voices, then runs an ensemble with a judge model picking the best result.
- VLM ensemble
- ffmpeg
- Python
RSNA Knee Abnormality Detection
Detecting knee abnormalities from RSNA medical imaging data.
- Medical imaging
- Deep learning
How we work
What happens after you email us
Four steps, in order. You see something running before the end.
- 01
Scope
We tell you what it takes, before any money moves.
- 02
Prototype
The riskiest part first, so it fails cheap if it is going to.
- 03
Build
Shipped in slices you can see running, not one delivery at the end.
- 04
Hand over
Documented and readable, so it is yours to maintain without us.
The team
Five engineers, all reachable
Amman, Jordan. Every profile below is public — check the work yourself.
Lana Alostath
Art / AI Engineer — Visual Design, Computer Vision
Aws Hanaqtah
System Engineer — Engines, CUDA, Transformers
Ayham al Suwi
ML Engineer — Tabular Models, Feature Engineering
Mamoun Yosef
AI Engineer — Vision Models, Medical Imaging
Chris Dart
🔊 Noise Composer / Sound Destructor
Tell Us What You Are Building
Describe the idea in your own words and we will work out which of the nine it needs.