SM Stratagem builds llm development for Dammam teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.
We build llm development for teams in Dammam that need working software, not a slide deck for next quarter's steering committee. The buyers we work with in Dammam tend to sit inside oil and gas, petrochemicals, and heavy industry, and they want ROI they can point to at a board meeting. So our default is LLM applications built with the boring engineering that keeps them alive in production, measured and iterated before anything touches production traffic. 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 Dammam and the wider GCC, so timezone, language, and data-residency get handled up front. What sets our llm development 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 already know the outcome you want, we can scope the first release inside a week and start building the week after.
Buyers in Dammam 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 Aramco-adjacent operators and heavy industry in Dammam, we build LLM apps that meets HSSE and vendor-approval gates from day one. Deployment lives close to plant systems, often on private cloud or on-prem.
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. Dammam is where the real industrial AI work sits, and it usually means meeting HSSE and vendor-approval standards from day one. We come in expecting those gates rather than surprised by them. Deployment lives close to plant systems, often on private cloud or on-prem, and the runbook we leave behind reflects that.
SM Stratagem builds predictive analytics in Dammam, Saudi Arabia. Predictions that inform decisions. Confidence intervals included. Deployed and monitored.
SM Stratagem builds nlp development in Dammam, Saudi Arabia. Text into structured signal. Arabic and English handled. Evaluated on your corpus.
SM Stratagem builds ai voice agents in Dammam, Saudi Arabia. Voice that finishes the task. Sub-second latency. Handover to humans, clean. 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 llm development in Sharjah, United Arab Emirates. LLM apps that survive production. Cost and latency instrumented. 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.