Models that know your business, running where you control them
We fine-tune, ground and deploy generative models on your own data, from Arabic-capable assistants to document and image models. Trained on our UAE GPUs, deployed in your accounts, and measured against tests you agree up front.
- Right for you if
- Off-the-shelf AI gives generic answers, or your data is too sensitive for public tools.
- Outcome
- A model that beats your baseline on your own test set, deployed where you control it.
- Timeline
- 6–14 weeks, depending on data readiness and scope.
- Pricing
- Fixed fee per phase, plus GPU time at cost-plus. Scoped after a discovery call.
Why most custom AI disappoints
Teams jump straight to "train our own model" when the real problem is retrieval, or they buy a generic chatbot when they needed a model that understands their documents, terms and tone.
The other common failure is having no test. Without an agreed evaluation set, nobody can say whether the new model is better than the old process, so it never earns trust.
We start with the test, not the model. Then we pick the cheapest approach that passes it: prompting, retrieval, fine-tuning or a full custom train, in that order.
Signs this is you
- Your team pastes documents into public chatbots to get answers.
- A generic assistant answers confidently and gets your products wrong.
- You need Arabic and English output that sounds like your brand.
- Regulators or clients require that data and models stay in your control.
- You have years of tickets, contracts or reports that nobody can search well.
The cheapest option that passes the test wins.
| Approach | When it fits | Typical cost | Time |
|---|---|---|---|
| Prompting a strong model | The task is general and data is not sensitive | Lowest | Days |
| Retrieval (RAG) | Answers depend on your documents, which change often | Low to medium | 3–6 weeks |
| Fine-tuning (LoRA / full) | You need a consistent format, tone, dialect or classification | Medium | 6–10 weeks |
| Private deployment | Data or regulation requires the model to run in your control | Medium to high | 4–8 weeks |
| Custom pre-training | Very rarely justified for a single business | High | Months |
What happens, week by week.
- Weeks 1–2
Define the job and the test
We agree what good looks like: 100 to 500 real examples with expected outputs. This becomes the yardstick every option is scored against.
You get: An evaluation set and a baseline score for the current process.
- Weeks 2–4
Prepare the data
Collect, clean, de-duplicate and label. Personal data is removed or masked before anything reaches a GPU.
You get: A versioned, documented training and retrieval dataset.
- Weeks 4–9
Build and compare
We try retrieval, fine-tuning (LoRA or full) and model choices side by side on our GPUs, and keep what scores best for the cost.
You get: A comparison report and a winning model candidate.
- Weeks 9–14
Red-team, deploy, monitor
Adversarial testing for leaks, bias and hallucination, then deployment behind your access controls, with monitoring and a feedback loop.
You get: A production endpoint, a model card and a monthly quality review.
What you walk away with.
Fine-tuned LLMs
Open-weight models such as Llama, Mistral, Qwen and Falcon adapted to your language, terms and tasks.
Retrieval (RAG) systems
Search over your documents with citations, permissions and freshness built in.
Arabic & bilingual models
Models evaluated on Gulf Arabic and code-switching, not just Modern Standard Arabic.
Vision & document AI
Extraction from invoices, IDs, forms and images, and on-brand image generation where it helps.
Included
- Use-case scoping and evaluation design
- Data audit, cleaning and PII masking
- Model selection across open-weight and commercial options
- Training and fine-tuning on UAE-hosted GPUs
- Safety and red-team testing
- Deployment to your cloud or our private hosting
- Model card, documentation and handover
- Monthly quality and drift review
Not included
- Training a foundation model from scratch (rarely worth it, we will tell you if it is)
- Data collection from third parties without consent
- Using your data to train models for anyone else
- Guaranteed outputs in regulated advice (medical, legal, financial)
How you'll know it worked.
- Score on the agreed evaluation set vs. baseline
- Hallucination and refusal rates
- Latency and cost per 1,000 requests
- Share of answers users accept without edits
Where this fits best.
From our insights.
RAG or fine-tuning? How to choose for your business data
They solve different problems. Picking the wrong one is the most expensive mistake we see in generative AI projects.
How many GPUs do you actually need? A sizing guide for fine-tuning and inference
Most teams over-buy for training and under-plan for inference. Some simple arithmetic gets you close before you talk to anyone.
Questions we hear a lot
Do we need to train our own model?
Usually not from scratch. Most business needs are met by a strong open or commercial model plus retrieval or light fine-tuning. We test the cheaper options first.
Who owns the model and weights?
You do. Fine-tuned weights, adapters, datasets and code are delivered into your accounts.
How good is the Arabic?
Good enough for production in most customer-facing tasks, including switching between Arabic and English mid-sentence. We test on Gulf dialect samples and tell you where it is weaker.
How much data do we need?
For fine-tuning, a few hundred to a few thousand good examples is often enough. For retrieval, whatever documents you already have.
Can the model run on our own servers?
Yes. We can size and deploy it on your hardware or private cloud, or host it for you on dedicated GPUs.
Start with a conversation, not a proposal.
Thirty minutes with someone who has built this before. If we're not the right fit, we'll say so and point you to who is.