We ship ai software development projects in Riyadh for teams that need working software this quarter, not a strategy deck for next.
In Riyadh, we run ai software development projects for operators who care about outcomes over demos and evaluation over adjectives. 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. Our approach is AI-enabled software products built the same way regular software is — with tests, review, deploys, and monitoring, wrapped in evaluation and monitoring so quality is a number your team owns, not a vibe check. 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 ai software 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.
The Saudi capital and Vision 2030 core is competitive, and Riyadh 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.
AI features live inside a normal codebase with normal tests, code review, PR discipline, and CI/CD. They ship the same way any other feature ships. No AI island.
Every AI feature has a p95 latency budget and a cost-per-request target. Product decisions are made with those numbers on the table.
AI features ship behind feature flags, get tested on a fraction of traffic first, and roll out cleanly. When something misbehaves, it's turned off in seconds.
You get a codebase your engineers can read, the AI-specific parts documented, and a runbook for the common failure modes. No black boxes.
Products where AI is central to the value — from a first working release through public launch, with the software discipline that keeps them alive after.
Add AI features (drafting, summarising, personalising, extracting) to a product you already ship, without destabilising the codebase around them.
Internal tools where AI is a first-class citizen — replacing spreadsheets, playbooks, and slow processes with software people actually want to use.
For Riyadh clients delivering Vision 2030 mandates, we build AI-enabled software 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 means building software where AI is a first-class feature, using the engineering practices that keep normal software alive: version control, code review, tests, staged deploys, feature flags, monitoring. Most 'AI projects' fail because they skip these — the AI part is treated as special. It isn't. It just has one extra dimension (model behaviour) that needs its own evals and monitoring.
AI development is the general umbrella. AI software development specifically means: the deliverable is a shipped software product with AI features, not a model or a Jupyter notebook. That framing matters because it changes what you build — you spend a lot of time on the software around the AI, not just on the AI itself.
Per-feature cost budgets, cheaper models for cheaper work, caching and prompt-level optimisation, and streaming so users don't pay for completions they don't wait for. Every AI feature has a dashboard showing cost per week and cost per active user, so when usage scales the finance conversation has real numbers, not surprises.
A first working release with one AI feature inside an existing product is usually four to six weeks. New AI-native products from zero to public launch typically run three to six months, depending on how much surrounding software (auth, billing, admin, integrations) has to be built alongside the AI. We ship weekly through both.
Yes. We prefer to work inside your codebase, following your conventions, using your CI/CD, going through your code review. That way what we build is legible to your team from day one and doesn't require a hand-over ceremony to maintain. If you don't have a codebase yet, we set one up in the shape we'd want to hand over — Next.js, TypeScript, Postgres, standard cloud, boring by design.
Yes. Most projects include the infra as part of the delivery — Terraform for the cloud setup, CI/CD for deploys, monitoring and error tracking wired in, and a runbook for the common failure modes. Handover includes access, secrets rotation, and a walkthrough so your team can operate the system from day one after we leave.
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.
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SM Stratagem builds generative ai development in Riyadh, Saudi Arabia. GenAI inside your product. Grounded on your data. Cost and latency measured.
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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.