One plant, one problem, one measurable KPI.
The projects we build and the model families behind them — all AI and ML, all deployed for real operations.
AI-driven digital transformation, Python first
SMHcoders FZ LLC is a systems-coding and data-analytics company based in Abu Dhabi, specialising in artificial intelligence and machine learning with a Python-first engineering practice. We work as a system integrator — making disparate technologies work in harmony so your operation runs efficiently.
AI and ML are no longer emerging technologies; they are essential tools for any business that wants to innovate, scale and lead. Our software helps companies optimise operations, automate complex tasks and draw predictive insight from their data.
- Custom, intelligent models aligned to your industry requirements
- Adaptive solutions that learn from your data over time
- Integration expertise across legacy and modern systems
Which model family fits your problem?
Pick the data you have and the question you're asking. The chooser names the model family we'd start with, what it outputs, and the delivered work closest to it — the same first conversation we have in a scoping call.
- Six families, matched on data type and question
- Points to the nearest delivered project or product
- A starting point — the data audit decides
Work we deliver
Live product-quality estimation for a crude column, validated against lab results.
Early-warning degradation models for gas-plant rotating equipment.
DXF/DWG engineering drawings converted to smart DEXPI format, tags indexed.
A chatbot that reasons over digitised drawings and answers in plain English.
Lightweight SaaS that finds and indexes instrument tags on engineering drawings.
Soiling and degradation flagged against expected output across a portfolio.
Short and long-horizon network demand forecasts with confidence bands.
Event logs, procedures and trends answered at the console, with sources cited.
Where our AI practice started
Before we specialised in heavy industry, the same team delivered AI across commerce, health, education and accessibility.
Neural networks and optimisation models applied to refinery processes for better energy efficiency and less waste.
AI chatbots integrated into support channels for instant assistance and faster response times.
Machine learning models predicting sales trends to optimise inventory and reduce stockouts.
AI analysis of wearable-device data for real-time insight and personalised recommendations.
Educational tools with AI-driven adaptive learning tuned to each student.
Speech-driven interfaces that improve digital access for people with disabilities.
Model families we work with
| Family | Typical use | Output |
|---|---|---|
| Time-series forecasters | Demand, load, quality ahead of time | Forecast + confidence band |
| Gradient-boosted regressors | Soft sensors, quality estimation | Fast, explainable estimate |
| Autoencoder anomaly detectors | Normal-behaviour monitoring across tags | Deviation score + ranked tags |
| Object detection networks | Gauges, PPE, defects, drawing symbols | Boxes, classes, alerts |
| Transformer NLP models | Extraction, classification, search (EN/AR) | Entities, labels, ranked results |
| Retrieval-augmented LLMs | Grounded assistants on private endpoints | Cited answers, audit log |
PyTorch
TensorFlow
scikit-learn
Hugging Face
OpenCV
OpenAIOne measurable use case, one owner.
Coverage, quality and gaps checked.
Proof of concept on your historical data.
Production-grade, tested, versioned.
Integrated, monitored, retrained.
“Every project here started as one plant, one problem, one measurable KPI.”
SMHcoders · method
Free 45-minute consultation with an engineer: bring one problem, leave with a feasibility read.
Are these real deployed projects?
They are representative engagements across our industries. In the consultation we walk through the ones closest to your use case, including what worked and what we would do differently.
What data do we need to start?
Most engagements start with 6–24 months of historian, lab or document data. We audit coverage in the first week and tell you plainly if it is not enough — before you commit.
How long until we see results?
A proof of concept on your historical data typically lands in 4–8 weeks. Production deployment follows once the KPI target is met and your team signs off.
Where does it run, and who sees our data?
On your infrastructure — plant-side servers, your Azure or AWS tenancy, or an air-gapped network. Data never leaves your boundary, and nothing is used to train third-party models.
Who owns the result?
You do. Handover includes source code, documentation, training sessions and a retraining plan.