AI Fine-Tuning in Doha, done the way it should be: scoped small, measured on real usage, and handed over with docs and runbooks your engineers can read.
In Doha, we run ai fine-tuning projects for operators who care about outcomes over demos and evaluation over adjectives. Qatar's LNG-funded economy rewards teams who can act on data quickly, and Doha operators tell us the same thing every quarter: less theatre, more delivery. In practice this looks like targeted fine-tuning that earns back its cost — usually smaller models on your specific task — the code we ship is boring by design and easy for the next engineer to read. The engineering is only half of it — we also leave you with the evals, the dashboards, and a rollback plan for the day something goes sideways. Our team ships from Dubai and delivers into Doha and the wider GCC, so timezone, language, and data-residency get handled up front. Our differentiator for ai fine-tuning in Doha is honest scoping — if the smallest useful version fits in a month, we say so and we build that first. If you already know the outcome you want, we can scope the first release inside a week and start building the week after.
The Doha projects that succeed have one thing in common: someone senior owns the outcome. We bring the engineering, the evals, and the on-call rota, but a business owner on your side is non-negotiable.
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 QatarEnergy contractors, QFC-registered firms, and ministries in Doha, we deliver AI fine-tuning inside existing vendor frameworks and procurement rules. Compliance is a delivery input, not a surprise at UAT.
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. Public-sector and energy-adjacent work in Doha typically requires sitting inside an existing vendor framework and delivering under strict procurement rules. That's the shape we default to. We don't take POC-only work in Qatar — it wastes everyone's time — so we scope for production from the start.
SM Stratagem builds generative ai development in Doha, Qatar. GenAI inside your product. Grounded on your data. Cost and latency measured. Book a scoping call.
SM Stratagem builds ai integration services in Doha, Qatar. AI inside the systems you already run. CRM, ERP, help desk, product. APIs, webhooks, and events.
SM Stratagem builds ai consulting in Doha, Qatar. Strategy that ends in a build. Roadmaps you can budget. Vendor-agnostic advice. Pilots that get shipped.
SM Stratagem builds ai fine-tuning in Sharjah, United Arab Emirates. Fine-tuning that earns back cost. Smaller, cheaper, faster models. Deployed on your infra.
SM Stratagem builds ai fine-tuning in Abu Dhabi, United Arab Emirates. Fine-tuning that earns back cost. Smaller, cheaper, faster models. Book a scoping call.
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.