Foundation Models, Trained on Your Domain
We fine-tune LLMs, vision models, and embedding models on your proprietary data — giving you a model that understands your vocabulary, regulations, and edge cases.
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Domain fine-tuning adapts a powerful foundation model to your specific industry vocabulary, regulatory context, and task requirements — without training from scratch. It is the fastest path from generic AI capability to domain-specific AI advantage.
When Fine-Tuning Is the Right Approach
Fine-tuning is appropriate when:
- You have domain vocabulary that general-purpose models mishandle: Arabic financial terms, medical ICD-10 codes, UAE property classifications, GCC retail SKU naming conventions
- You need consistent output format: structured extraction from unstructured documents requires format consistency that prompt engineering alone cannot reliably achieve at scale
- Cost and latency matter: a fine-tuned 7B model running on your infrastructure costs 95% less and responds 10x faster than GPT-4 API calls at production volume
- Data privacy is required: your documents cannot be sent to an external API — fine-tuning allows you to run inference entirely within your own environment
Arabic Language Fine-Tuning
Arabic NLP is a specific challenge that most Western AI vendors handle poorly. Modern Standard Arabic and Emirati dialect differ significantly from each other and from the training distributions of models like GPT-4. UAE financial documents mix Arabic and English within the same sentence. Healthcare records use Arabic medical terminology with no direct English equivalent.
Our Arabic fine-tuning pipeline starts from Arabic-pretrained base models with genuine multilingual representation, fine-tunes on UAE-specific domain corpora, and evaluates against Arabic-specific benchmarks rather than relying on English-language evaluation sets that miss dialect and domain errors.
Engagement Phases
Dataset Curation & Preparation
Curate and prepare fine-tuning dataset from your domain corpus. Format for instruction tuning or continued pre-training. Quality filtering, deduplication, and format validation.
Fine-Tuning & Evaluation
Fine-tune base model using LoRA/QLoRA for parameter-efficient training. Evaluate against task-specific benchmarks. Compare to base model and GPT-4 baseline on your evaluation set.
Deployment & Integration
Package fine-tuned model for inference. Deploy to your cloud environment or on-premises infrastructure. API wrapper, authentication, and latency optimisation.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Task Accuracy | GPT-4 on UAE compliance documents: 74% accuracy on entity extraction | Fine-tuned model: 91% accuracy — trained on 2,000 annotated UAE compliance docs |
| Inference Cost | GPT-4 API: AED 0.18 per document processed | Deployed fine-tuned model: AED 0.009 per document — 95% cost reduction |
| Latency | GPT-4 API: 2-8 second response time with rate limiting | Self-hosted fine-tuned model: 180ms average — suitable for real-time workflows |
Tools We Use
Frequently Asked Questions
What is the difference between fine-tuning and prompt engineering?
Prompt engineering adjusts the input to a general-purpose model at inference time. Fine-tuning updates the model weights using your domain data — the model learns your vocabulary, style, and task patterns during training. Fine-tuning produces a model that outperforms prompt engineering on domain tasks, runs 10-100x faster (smaller model, no long system prompts), and costs far less at scale. For simple tasks on low volumes, prompt engineering is sufficient. For high-volume production workloads or tasks requiring deep domain knowledge, fine-tuning is the correct approach.
Which base models do you fine-tune?
We work with open-weight models (Llama 3, Mistral, Phi-3, Qwen2) for deployments requiring on-premises or private cloud hosting, and with API-based fine-tuning (OpenAI fine-tuning API, Gemini fine-tuning) for clients using managed infrastructure. For Arabic language tasks, we start from Arabic-pretrained base models (AraBERT, AraT5, Jais) rather than fine-tuning Western-trained models with weak Arabic foundations.
How much data do I need for fine-tuning?
For instruction fine-tuning (adapting an LLM to follow domain-specific instructions): 500–5,000 high-quality examples is typically sufficient with LoRA. For continued pre-training on domain text: 1M+ tokens of domain corpus. For embedding model fine-tuning (improving retrieval): 1,000–10,000 query-document pairs. We assess your data in the first week and recommend the appropriate approach for your volume and quality.
Can fine-tuned models be run on-premises in the UAE?
Yes. Fine-tuned open-weight models can be deployed entirely within your UAE data centre or private cloud — no data leaves your environment at inference time. This is the preferred deployment model for clients with DIFC Data Protection Law, DHA health data, or CBUAE financial data constraints. We deploy using vLLM or Text Generation Inference (TGI) on your Kubernetes cluster with GPU nodes.
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.
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