For operators in Abu Dhabi, 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 Abu Dhabi that need working software, not a slide deck for next quarter's steering committee. Abu Dhabi's pull for us is public sector and Mubadala-adjacent enterprise, and briefs coming out of ADGM and the ministries rarely want another pilot that dies before rollout. So our default is generative AI features that ship inside your product, not standalone demos, measured and iterated before anything touches production traffic. Every project ships with docs, evals, and a runbook the next engineer can pick up cold, without a knowledge-transfer week. We deliver across United Arab Emirates and the GCC in English and Arabic, with a project lead who owns delivery end-to-end rather than a chain of handoffs. The reason clients bring us back for the second and third generative ai development project is the handover: docs, evals, runbook, and a person who picks up the phone. If you're comparing agencies, ask us how we measure success before we quote — that's usually the fastest way to see who's serious.
Buyers in Abu Dhabi 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 entities inside ADGM and the Abu Dhabi government, we build generative AI that respects data-residency, vendor-review, and procurement rules from the SoW onward. The cloud region and audit trail get decided before the first line of code.
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. We deliver into ADGM-registered entities and Abu Dhabi government departments on a regular basis, which usually means tighter data-residency and vendor-review controls. We plan for those constraints inside the SoW rather than trying to bolt them on right before go-live, so audits and reviews rarely become the bottleneck.
SM Stratagem builds ai software development in Abu Dhabi, United Arab Emirates. AI features, software discipline. Tests, review, deploys, monitoring.
SM Stratagem builds machine learning development in Abu Dhabi, United Arab Emirates. ML that reaches production. Monitored for drift and quality.
SM Stratagem builds ai chatbot development in Abu Dhabi, United Arab Emirates. Grounded on your product docs. Web, WhatsApp, and Slack. Evaluated on every push.
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 generative ai development in Sharjah, 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.