Custom ML Models Built for Your Industry, Your Data
We build vertical AI models from the ground up — trained on your domain data, deployed in your infrastructure, and optimised for your specific business objective.
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Vertical AI model development is the core of what we do at mlai.ae. We build machine learning models from the ground up — not API wrappers around general-purpose LLMs, but production models trained on your data, deployed in your infrastructure, and tuned to your specific business objective.
Why Vertical AI Outperforms Generic Models
A generic foundation model like GPT-4 was trained on internet-scale text. It knows a great deal about fraud in general but nothing specific about your card scheme, your customer base, or the fraud patterns prevalent in UAE fintech. A vertical model trained on your 3-year transaction history with UAE card data knows exactly those patterns — and will outperform the generic model on your data consistently.
The performance gap is measurable: on domain-specific tasks, fine-tuned or purpose-built vertical models typically achieve 15–30% higher accuracy than prompted general-purpose models. On structured prediction tasks (tabular data), purpose-built gradient boosting or deep learning models routinely outperform LLMs by a wider margin.
Our Development Methodology
Every build follows a five-phase methodology:
Phase 1 — Problem Framing: We translate your business objective into a precise ML problem statement. “Reduce fraud losses” becomes “binary classification on transaction features with false positive rate under 5% at 90% recall.” Precision in framing prevents scope creep and misaligned deliverables.
Phase 2 — Data Pipeline: We build reproducible data ingestion, cleaning, and transformation pipelines before touching model code. Data pipelines are version-controlled, tested, and documented — they run automatically in production without manual intervention.
Phase 3 — Feature Engineering: The most underappreciated phase of ML. Domain knowledge shapes features: for fintech, velocity features, device fingerprinting, and merchant category patterns. For proptech, location features, DLD transaction history, and infrastructure proximity scores. Good features produce better models than more complex architectures on mediocre features.
Phase 4 — Model Development: We train, evaluate, and iterate. Every candidate model is benchmarked against your current system (rule-based, manual, or previous model) and against holdout test data. We document model performance on sub-populations — to identify bias before deployment.
Phase 5 — Production Deployment: Model packaged as a REST inference API, deployed to your cloud environment, with monitoring dashboards and automated retraining triggers configured from day one.
Engagement Phases
Data Pipeline & Feature Engineering
Build data ingestion, cleaning, and transformation pipelines. Engineer domain-specific features with business subject matter expert input. Create train/validation/test splits with appropriate stratification.
Model Development & Evaluation
Train, evaluate, and iterate on model architecture. Benchmark against baseline and existing rule-based systems. Bias and fairness evaluation. UAE regulatory compliance check.
Production Deployment
Package model as inference API. Deploy to client cloud environment. MLOps pipeline setup: automated retraining triggers, monitoring dashboards, and rollback capability.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Model Accuracy | Rule-based system: 71% precision on fraud detection | Custom model: 94% precision, 89% recall — same data |
| Time to Insight | Manual review queue: 4-6 hours per case | Automated scoring: real-time inference at <200ms |
| False Positive Rate | Rule-based: 18% false positive rate blocking legitimate transactions | Custom model: 4% false positive rate — fewer blocked customers |
Tools We Use
Frequently Asked Questions
What types of AI models does mlai.ae build?
We build supervised and semi-supervised models for classification (fraud detection, disease classification, lead scoring), regression (property valuation, demand forecasting, credit scoring), anomaly detection (transaction monitoring, equipment fault detection), and natural language processing (document classification, entity extraction, sentiment analysis for Arabic content). We also fine-tune foundation models (LLMs, vision transformers) on domain-specific datasets. Use case determines architecture — we recommend the right model type for your problem, not the most complex one.
How long does a custom AI model take to build?
8–16 weeks from data access to production deployment. The main variable is data readiness: if your data is clean, labelled, and accessible via API or data warehouse, we can deliver a first prototype in 4 weeks and a production deployment in 12. If data cleaning, labelling, or pipeline work is needed first, timeline extends accordingly. We run an AI Readiness Assessment to determine this before the build starts.
Who owns the model and the training data?
You do. All models, weights, training pipelines, and associated code are delivered to you under full IP transfer. We do not retain copies of your training data or model weights. Engagement concludes with a handover package: model artefacts, training code, inference API, and documentation sufficient for your team to operate and retrain the model independently.
How do you handle Arabic language and UAE-specific data?
UAE data has specific characteristics: Arabic text (MSA and Emirati dialect), Arabic numerals in some systems, UAE-specific date formats, IBAN structures, Emirates ID formats, and regulatory codes (CBUAE, DHA, DLD). Our models are tested against these patterns. For NLP tasks, we use Arabic-pretrained models (AraBERT, CAMeL) and fine-tune on UAE-specific corpora rather than applying Western-trained models with poor Arabic performance.
Build It. Run It. Own It.
Book a free 30-minute AI discovery call with our Vertical AI experts in Dubai, UAE. We scope your first model, estimate data requirements, and show you the fastest path to production.
Talk to an Expert