For operators in Abu Dhabi, we run ai fine-tuning projects that leave you with production systems your team can maintain, not a vendor-only black box.
We build ai fine-tuning for teams in Abu Dhabi that need working software, not a slide deck for next quarter's steering committee. Most briefs we see out of Abu Dhabi come from energy, government, and finance — the vertical shifts, but the shape of the problem does not. So our default is targeted fine-tuning that earns back its cost — usually smaller models on your specific task, measured and iterated before anything touches production traffic. By the time we hand over, the system is deployed on your cloud, monitored on your dashboards, and covered by tests your engineers can read. We work in your timezone, we speak the vendor landscape in United Arab Emirates, and we know which cloud regions actually keep data on-shore. We keep ai fine-tuning teams small on purpose — usually three to five people on your project — so the person building understands the full system, not just their slice. If the project has already stalled once, the shape of the first release was usually wrong — that's fixable in a week, not a quarter.
Buyers in Abu Dhabi are done with pilots. What they want now is one production system, measured, running, and reducing a real cost line or lifting a real revenue line. That's the frame we work inside.
Fine-tuning is expensive to run and maintain. We recommend it only when it's cheaper or better than prompting and RAG on your workload — and we're honest when it isn't.
Most of our fine-tuning work takes a small open-source model and gets it to beat GPT-4-class quality on a specific task, at a fraction of the per-token cost.
Every fine-tune ships with a head-to-head evaluation against the pre-tune model and the closed-model baseline. If the fine-tune doesn't win on your metric, we don't ship it.
Fine-tuned models drift as your data and product change. We build the retraining loop into the delivery so the model stays fresh without heroics.
Fine-tune a small model to draft product descriptions, marketing copy, or standard responses in your tone, at closed-model quality but 10x cheaper.
Fine-tune on your labelled data to beat generic models on domain-specific classification, extraction, and routing tasks.
Fine-tune multilingual models on your Arabic-English corpus to handle the dialects and mixed-language input your customers actually use.
For entities inside ADGM and the Abu Dhabi government, we build AI fine-tuning that respects data-residency, vendor-review, and procurement rules from the SoW onward. The cloud region and audit trail get decided before the first line of code.
Three cases. First, when you're serving high-volume inference and per-token cost of closed models is unsustainable — a fine-tuned small model can be 10-50x cheaper. Second, when you need a specific style or output format that prompting doesn't reliably produce. Third, when data can't leave your infrastructure and you need to beat what open-source can do out of the box. Outside these cases, prompting and RAG are usually better.
For LoRA-style fine-tuning on a small model, a few hundred to a few thousand well-labelled examples per task is often enough. Full fine-tuning of larger models needs more. We start with the smallest experiment that can tell you if fine-tuning helps, before spending the budget for the full run.
For most projects, a first fine-tune with iteration costs less than a full engineering month. The bigger cost is data preparation and evaluation. We estimate both up front and run a small experiment first to confirm the approach before committing to the full budget.
Yes, via the fine-tuning APIs OpenAI and Anthropic provide. That's often the quickest way to test whether fine-tuning helps at all, before investing in the open-source path. For long-term production use we usually recommend open-source fine-tunes for cost and control reasons, but the closed-model fine-tune is a fast way to prove value.
For a well-scoped single-task fine-tune, expect four to six weeks end-to-end: data preparation, a baseline evaluation on the pre-tune model, an initial fine-tune, a few rounds of iteration against the eval set, and a deployment path onto your infrastructure or ours. Longer projects add multi-task tuning, more sophisticated data pipelines, evaluation on adversarial cases, and continuous retraining infrastructure that keeps the model current as your data shifts.
The retraining pipeline is treated as a delivery output — scheduled data collection, a labelling process (or model-graded auto-labelling), retrain runs, evaluation against the current production model, and a promotion gate. It's the same discipline we use for classical ML models, applied to fine-tuned LLMs.
Yes. We deliver into ADGM-registered entities and Abu Dhabi government departments on a regular basis, which usually means tighter data-residency and vendor-review controls. We plan for those constraints inside the SoW rather than trying to bolt them on right before go-live, so audits and reviews rarely become the bottleneck.
SM Stratagem builds ai data engineering in Abu Dhabi, United Arab Emirates. Data pipelines that don't rot. Quality measured, not assumed. Book a scoping call.
SM Stratagem builds llm development in Abu Dhabi, United Arab Emirates. LLM apps that survive production. Cost and latency instrumented. Book a scoping call.
SM Stratagem builds ai development in Abu Dhabi, United Arab Emirates. Discovery to deployment. Measured on real usage. Handover-ready. Docs and evals included.
SM Stratagem builds ai fine-tuning in Dammam, Saudi Arabia. Fine-tuning that earns back cost. Smaller, cheaper, faster models. Evaluated against baseline.
SM Stratagem builds ai fine-tuning in Muscat, Oman. Fine-tuning that earns back cost. Smaller, cheaper, faster models. Evaluated against baseline.
We'll scope the first release, define the eval set, and give you a build plan you can hand to any engineering team — ours or yours.