SM Stratagem builds ai fine-tuning for Riyadh teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.
We build ai fine-tuning for teams in Riyadh that need working software, not a slide deck for next quarter's steering committee. The Saudi capital and Vision 2030 core rewards teams who can act on data quickly, and Riyadh operators tell us the same thing every quarter: less theatre, more delivery. 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. Every project ships with docs, evals, and a runbook the next engineer can pick up cold, without a knowledge-transfer week. Our team ships from Dubai and delivers into Riyadh and the wider GCC, so timezone, language, and data-residency get handled up front. What sets our ai fine-tuning delivery apart is that the engineer who scopes the build is the same engineer who ships it and shows up at the go-live call. If you have a rough brief, we can turn it into a build plan without a two-month discovery phase that nobody remembers by launch.
Buyers in Riyadh 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 Riyadh clients delivering Vision 2030 mandates, we build AI fine-tuning that clears NCA and SDAIA guidance, sits in a Saudi-region cloud, and integrates with the Tier-1 banking and ministry stack that most programmes already run on.
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. For Riyadh clients we default to Saudi-region cloud (AWS or GCP in KSA), work with local Saudi partners where Saudization requires it, and design for NCA and SDAIA guidance from the start of the engagement. The regulatory shape is treated as a delivery input, not something we discover at UAT.
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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.