SM Stratagem builds llm development for Riyadh teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.
We build llm development for teams in Riyadh that need working software, not a slide deck for next quarter's steering committee. Most briefs we see out of Riyadh come from government, banking, and Vision 2030 programmes — the vertical shifts, but the shape of the problem does not. In practice this looks like LLM applications built with the boring engineering that keeps them alive in production — 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. The team is remote-friendly but in-region, so travel to Riyadh for workshops and go-live is standard, not a favour we ask for. 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 the project has already stalled once, the shape of the first release was usually wrong — that's fixable in a week, not a quarter.
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.
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 Riyadh clients delivering Vision 2030 mandates, we build LLM apps 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.
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. 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.
SM Stratagem builds ai consulting in Riyadh, Saudi Arabia. Strategy that ends in a build. Roadmaps you can budget. Vendor-agnostic advice. Book a scoping call.
SM Stratagem builds ai integration services in Riyadh, Saudi Arabia. AI inside the systems you already run. CRM, ERP, help desk, product. Book a scoping call.
SM Stratagem builds generative ai development in Riyadh, Saudi Arabia. GenAI inside your product. Grounded on your data. Cost and latency measured.
SM Stratagem builds llm development in Dubai, United Arab Emirates. LLM apps that survive production. Cost and latency instrumented. 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.
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.