For operators in Riyadh, we run generative ai development projects that leave you with production systems your team can maintain, not a vendor-only black box.
We build generative ai development for teams in Riyadh that need working software, not a slide deck for next quarter's steering committee. The buyers we work with in Riyadh tend to sit inside government, banking, and Vision 2030 programmes, and they want ROI they can point to at a board meeting. So our default is generative AI features that ship inside your product, not standalone demos, measured and iterated before anything touches production traffic. By the time we hand over, the system is deployed on your cloud, monitored on your dashboards, and covered by tests your engineers can read. We work in your timezone, we speak the vendor landscape in Saudi Arabia, and we know which cloud regions actually keep data on-shore. Our differentiator for generative ai development in Riyadh 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 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.
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 Riyadh clients delivering Vision 2030 mandates, we build generative AI 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.
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. 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 automation in Riyadh, Saudi Arabia. Real workflows, automated. Human review where it matters. Rollback and audit built in.
SM Stratagem builds rag development in Riyadh, Saudi Arabia. Retrieval that finds the right doc. Grounded, cited generation. Ingestion pipeline you own.
SM Stratagem builds mlops services in Riyadh, Saudi Arabia. ML delivery, repeatable. Training pipelines you own. Monitoring and drift built in.
SM Stratagem builds generative ai development in Dubai, United Arab Emirates. GenAI inside your product. Grounded on your data. Cost and latency measured.
SM Stratagem builds generative ai development in Abu Dhabi, United Arab Emirates. GenAI inside your product. Grounded on your data. Cost and latency measured.
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.