SM Stratagem builds generative ai development for Dammam teams that care about deployment, evaluation, and monitoring — not just the demo that impresses the boardroom.
We build generative ai development for teams in Dammam that need working software, not a slide deck for next quarter's steering committee. Most briefs we see out of Dammam come from oil and gas, petrochemicals, and heavy industry — the vertical shifts, but the shape of the problem does not. In practice this looks like generative AI features that ship inside your product, not standalone demos — the code we ship is boring by design and easy for the next engineer to read. Every project ships with docs, evals, and a runbook the next engineer can pick up cold, without a knowledge-transfer week. We work in your timezone, we speak the vendor landscape in Saudi Arabia, and we know which cloud regions actually keep data on-shore. We keep generative ai development teams small on purpose — usually three to five people on your project — so the person building understands the full system, not just their slice. 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 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.
GenAI features that live inside your existing product surface, using your auth, your billing, your data, and your design system — not a separate app users have to discover.
Every GenAI feature has a cost-per-request and a p95 latency budget from day one. When usage scales, the finance conversation doesn't become a fire drill.
Users don't wait ten seconds staring at a blank screen. We stream, we degrade gracefully when the model provider is slow, and we cache what's safe to cache.
GenAI that draws from your own content, your own tone of voice, and your own product constraints, not a generic assistant that could just as easily be a competitor's.
Drafting, rewriting, and summarising inside your product, respecting your data, your tone, and your permissions model.
Marketing copy, images, and structured briefs generated in-brand and reviewed by a human before publish.
Generated recommendations, explanations, or nudges tailored to the customer, with the reasoning surfaced so trust is earned.
For Aramco-adjacent operators and heavy industry in Dammam, we build generative AI that meets HSSE and vendor-approval gates from day one. Deployment lives close to plant systems, often on private cloud or on-prem.
A chatbot is one shape a generative AI system can take. Generative AI more broadly covers any feature where the output is created rather than looked up — drafting, summarising, generating images, personalising messages, translating tone. The engineering discipline is the same: ground on your data, budget cost and latency, evaluate quality, monitor in production.
Cost budgets per feature, cheaper models for cheaper work, caching where the output is safe to cache, and streaming so users don't pay for full completions they don't wait for. We instrument every call so you can see cost per feature per week and catch drift before it lands on a bill.
A well-scoped feature inside an existing product is usually two to four weeks to first release. That covers prototyping, integration with your auth and data, cost and latency instrumentation, and an initial eval set. Timelines stretch when the surrounding product is missing pieces — auth, billing, tenant isolation — that we then have to build first.
Both. Text is the majority of what we ship because it fits the most business use cases, but we build image generation (Stable Diffusion, DALL·E, Midjourney API) and increasingly short-form video generation for marketing and personalisation use cases. Same engineering discipline, different models.
Model choice matters here. We use providers whose terms allow commercial use of generated content, we track provenance in the metadata of anything we produce, and for high-stakes content (legal, medical, regulated marketing) we recommend human review before publish. If you have specific IP constraints we design the model choice around them.
Yes, and we recommend it. The pattern that works is ship one GenAI feature end-to-end — instrumented, budgeted, evaluated — and use it as the template for the next three. That's cheaper than trying to design a platform up front for features you haven't validated yet.
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 ai development in Dammam, Saudi Arabia. Discovery to deployment. Measured on real usage. Handover-ready. Docs and evals included.
SM Stratagem builds computer vision development in Dammam, Saudi Arabia. Vision for real environments. Edge or cloud, your call. Handles lighting and drift.
SM Stratagem builds ai agent development in Dammam, Saudi Arabia. Agents that take real actions. Permissioned and logged. Rollback baked in.
SM Stratagem builds generative ai development in Kuwait City, Kuwait. GenAI inside your product. Grounded on your data. Cost and latency measured.
SM Stratagem builds generative ai development in Doha, Qatar. GenAI inside your product. Grounded on your data. Cost and latency measured. 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.