We ship rag development projects in Sharjah for teams that need working software this quarter, not a strategy deck for next.
For Sharjah companies, we treat rag development as engineering — versioned, tested, monitored — not as a science project you renew every year. Most briefs we see out of Sharjah come from manufacturing, education, and logistics — the vertical shifts, but the shape of the problem does not. 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. 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. We keep rag 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 already know the outcome you want, we can scope the first release inside a week and start building the week after.
The northern emirates' industrial belt is competitive, and Sharjah 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.
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 Sharjah manufacturers and family groups, we retrofit RAG systems onto existing SAP or Oracle installs without ripping anything out. We start with one plant or one process, prove the lift, then roll out — the same pattern that survives change-management review.
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 — manufacturers and family holdings make up a large share of our Sharjah delivery. The typical brief is retrofitting AI onto an SAP or Oracle install without disrupting operations. We start with one plant or one workflow, prove the lift with real numbers, then roll out across the group. That pattern survives change management.
SM Stratagem builds custom chatgpt development in Sharjah, United Arab Emirates. Private ChatGPT, your data. Deployed on your infra. Grounded and cited.
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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.