For operators in Jeddah, we run rag development projects that leave you with production systems your team can maintain, not a vendor-only black box.
We build rag development for teams in Jeddah that need working software, not a slide deck for next quarter's steering committee. Jeddah's pull for us is commerce and Red Sea giga-projects, and trading houses and NEOM-adjacent operators rarely want another pilot that dies before rollout. Our approach is retrieval-augmented generation that actually retrieves the right thing before it generates, wrapped in evaluation and monitoring so quality is a number your team owns, not a vibe check. We handle infrastructure, evaluation, and handover so your team owns the system after we leave, not a black box only we understand. We work in your timezone, we speak the vendor landscape in Saudi Arabia, and we know which cloud regions actually keep data on-shore. What sets our rag development delivery apart is that the engineer who scopes the build is the same engineer who ships it and shows up at the go-live call. 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 Jeddah 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.
Most bad RAG systems are actually bad retrieval systems dressed up as bad generation. We fix retrieval first — chunking, embedding, hybrid search, reranking — before touching prompts.
The ingestion pipeline is versioned, idempotent, and re-runnable. When your document set changes, the index updates without an engineer having to remember what they did last time.
Every generated answer comes back with the sources it used and the page or section it pulled from. Users get to check the work; auditors get a trail.
The eval set is real questions from real users, graded against your documents. We track retrieval@k, answer faithfulness, and refusal rate as the three headline numbers.
Q&A over product docs, policies, or knowledge bases, with citations and a clean refusal when the answer isn't in the corpus.
Retrieval across long-form legal, HR, or regulatory documents, returning the passage and the answer together.
Support bots grounded on ticket history and help-centre content, with clean escalation when they can't answer.
For Jeddah trading houses and Red Sea tourism operators, we build RAG systems that handles bilingual customer flows, connects to legacy trade systems, and scales into giga-project-adjacent programmes without a rebuild.
Because at any scale — more than a few hundred documents, more than a handful of users, or documents that update — the ChatGPT approach breaks. You lose control of retrieval quality, you can't measure it, you can't fix it when it's wrong, and you can't hold onto your data. A proper RAG system solves all four.
A first working version on a defined corpus is usually three to five weeks. That covers the ingestion pipeline, chunking and embedding strategy, hybrid retrieval, generation prompt, evaluation set, and a simple UI or API. Longer projects handle bigger corpora, multi-tenancy, live updates, and multi-language retrieval.
For most projects PostgreSQL + pgvector is enough and keeps the operational surface small. When you need serious scale or advanced filtering, Weaviate, Qdrant, or Pinecone become worthwhile. We choose based on your document volume, update frequency, and where your ops team already has muscle memory.
The ingestion pipeline is designed to be idempotent — you can re-ingest a document and it replaces the old version cleanly, without stale chunks floating around. For high-frequency updates we set up scheduled reingestion or webhook-triggered reingestion so the index stays fresh.
Yes. Arabic retrieval needs a bit more care with tokenisation and embedding model choice — some multilingual embedding models are much stronger than others on Arabic. We benchmark on your actual corpus rather than trusting the vendor's marketing, and we ship bilingual RAG systems that retrieve across Arabic and English source material.
First, retrieval quality — most bad answers start with bad retrieval. Second, prompting the model to refuse when the retrieved context doesn't support an answer. Third, an eval set that specifically tests refusal cases. Fourth, monitoring in production that flags answers with low retrieval confidence for review.
Yes. Jeddah briefs usually mix legacy trade systems, bilingual customer flows, and giga-project-adjacent programmes along the Red Sea coast. We've delivered across all three shapes and we're comfortable operating in vendor frameworks that expect a Saudi-region deployment and Arabic-first user flows.
SM Stratagem builds generative ai development in Jeddah, Saudi Arabia. GenAI inside your product. Grounded on your data. Cost and latency measured.
SM Stratagem builds ai integration services in Jeddah, Saudi Arabia. AI inside the systems you already run. CRM, ERP, help desk, product. Book a scoping call.
SM Stratagem builds machine learning development in Jeddah, Saudi Arabia. ML that reaches production. Monitored for drift and quality. Retraining on a schedule.
SM Stratagem builds rag development in Dubai, United Arab Emirates. Retrieval that finds the right doc. Grounded, cited generation. Ingestion pipeline you own.
SM Stratagem builds rag development in Dammam, Saudi Arabia. Retrieval that finds the right doc. Grounded, cited generation. Ingestion pipeline you own.
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.