We ship llm development projects in Doha for teams that need working software this quarter, not a strategy deck for next.
In Doha, we run llm development projects for operators who care about outcomes over demos and evaluation over adjectives. Doha's pull for us is public sector and QatarEnergy-adjacent enterprise, and ministries, QIA-backed operators, and QFC-registered firms rarely want another pilot that dies before rollout. 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. We handle infrastructure, evaluation, and handover so your team owns the system after we leave, not a black box only we understand. Our team ships from Dubai and delivers into Doha and the wider GCC, so timezone, language, and data-residency get handled up front. The reason clients bring us back for the second and third llm development project is the handover: docs, evals, runbook, and a person who picks up the phone. If you already know the outcome you want, we can scope the first release inside a week and start building the week after.
Qatar's LNG-funded economy is competitive, and Doha operators don't get credit for AI theatre. What ships and reduces cost — or lifts revenue — is what earns the next budget round, and that's what we optimise for.
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 QatarEnergy contractors, QFC-registered firms, and ministries in Doha, we deliver LLM apps inside existing vendor frameworks and procurement rules. Compliance is a delivery input, not a surprise at UAT.
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. 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 ai voice agents in Doha, Qatar. Voice that finishes the task. Sub-second latency. Handover to humans, clean. Evaluated on call outcomes.
SM Stratagem builds ai agent development in Doha, Qatar. Agents that take real actions. Permissioned and logged. Rollback baked in. Book a scoping call.
SM Stratagem builds ai fine-tuning in Doha, Qatar. Fine-tuning that earns back cost. Smaller, cheaper, faster models. Evaluated against baseline.
SM Stratagem builds llm development in Muscat, Oman. LLM apps that survive production. Cost and latency instrumented. Prompts versioned and evaluated.
SM Stratagem builds llm development in Manama, Bahrain. 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.