SM Stratagem builds llm development for Dubai teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.
We build llm development for teams in Dubai that need working software, not a slide deck for next quarter's steering committee. The buyers we work with in Dubai tend to sit inside financial services, real estate, and logistics, and they want ROI they can point to at a board meeting. That means LLM applications built with the boring engineering that keeps them alive in production, with clear ownership of what runs in production and who fixes it when something breaks. 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 Dubai and the wider GCC, so timezone, language, and data-residency get handled up front. Our differentiator for llm development in Dubai is honest scoping — if the smallest useful version fits in a month, we say so and we build that first. 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 Dubai 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.
Prompts live in version control, get reviewed like any other change, and are tested against an eval set before they ship. No magic strings buried in the codebase.
Every LLM feature ships with a cost-per-request target and a monthly ceiling. When usage grows, you know before finance does.
The architecture doesn't marry you to one model provider. Swapping OpenAI for Claude, or bringing in an open-source model on your infra, is a config change plus an eval run — not a rebuild.
Full tracing of every LLM call — inputs, outputs, cost, latency, tokens — surfaced in a dashboard your team can query. Debugging isn't a séance.
Drafting, summarising, extracting, and classifying features built inside your existing product and instrumented for cost and quality.
A shared LLM layer for your product and engineering teams, with prompt versioning, evaluation, and cost tracking, so every team doesn't rebuild the same wrapper.
Chains and agents that decompose a task, call tools, and produce a checked output — with retries, timeouts, and observability wired in.
For SaaS operators in DIFC, DMCC, and the tech free zones, we ship LLM apps that plugs into the product you already sell, not a demo bolted on top. Auth, billing, and multi-tenant data separation are treated as day-one requirements, not backlog items.
It depends on the workload. For most business tasks, Claude and GPT-4-class models via API are the fastest way to ship. For high-volume, low-margin tasks or strict data-residency needs, open-source models (Llama, Mistral, Qwen) on your infra become cheaper past a certain scale. We benchmark on your eval set rather than the vendor's, so the choice is grounded in your workload.
Usually not to start. Retrieval-augmented generation (RAG) and careful prompting cover 80% of what people want to fine-tune for, and they're cheaper and easier to iterate. Fine-tuning becomes worthwhile when you have a stable, high-volume task, a proprietary output style, or cost pressure at scale. We recommend it only when it will actually earn back the effort.
A test set that reflects real usage, with graders that check the properties you care about — factual grounding, format, tone, refusal in the right cases. Grading is done with a mix of exact-match, model-graded, and human-labelled checks depending on what you're testing. Every prompt or model change runs against the eval set before it ships.
Treat every user input as untrusted, validate outputs before they touch downstream systems, isolate tool permissions so an injected prompt can't drive a destructive action, and monitor for the patterns you know about. We also run adversarial evals to catch new failure modes before users find them.
A single well-scoped LLM feature is usually three to six weeks. Multi-feature platforms take longer because the platform layer — prompt versioning, evals, observability, cost tracking — is more work than any single feature. The order matters: ship one feature end-to-end first, then extract the reusable platform pieces from it.
Both, depending on the shape. LangChain and LangGraph accelerate multi-step agents and complex chains. For simpler single-shot features, a small custom wrapper is easier to maintain than a framework we only use a slice of. We choose per feature, not per company.
Yes — most of our client base sits in DIFC, DMCC, JAFZA, and Dubai Internet City. Vendor onboarding and procurement look different in each free zone, and we've been through them enough times to move faster than a firm doing it for the first time. Contracts, POs, and invoicing route through a UAE mainland entity we already run.
SM Stratagem builds ai data engineering in Dubai, United Arab Emirates. Data pipelines that don't rot. Quality measured, not assumed. Access controlled by user.
SM Stratagem builds rag development in Dubai, United Arab Emirates. Retrieval that finds the right doc. Grounded, cited generation. Ingestion pipeline you own.
SM Stratagem builds ai development in Dubai, United Arab Emirates. Discovery to deployment. Measured on real usage. Handover-ready. Docs and evals included.
SM Stratagem builds llm development in Kuwait City, Kuwait. LLM apps that survive production. Cost and latency instrumented. Prompts versioned and evaluated.
SM Stratagem builds llm development in Doha, Qatar. LLM apps that survive production. Cost and latency instrumented. Prompts versioned and evaluated.
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.