RAG Development in Riyadh, done the way it should be: scoped small, measured on real usage, and handed over with docs and runbooks your engineers can read.
In Riyadh, we run rag development projects for operators who care about outcomes over demos and evaluation over adjectives. Most briefs we see out of Riyadh come from government, banking, and Vision 2030 programmes — the vertical shifts, but the shape of the problem does not. In practice this looks like retrieval-augmented generation that actually retrieves the right thing before it generates — the code we ship is boring by design and easy for the next engineer to read. 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 deliver across Saudi Arabia and the GCC in English and Arabic, with a project lead who owns delivery end-to-end rather than a chain of handoffs. Our differentiator for rag 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're comparing agencies, ask us how we measure success before we quote — that's usually the fastest way to see who's serious.
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.
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 Riyadh clients delivering Vision 2030 mandates, we build RAG systems 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.
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. 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 enterprise ai solutions in Riyadh, Saudi Arabia. AI that clears governance. Vendor-review ready. Multi-tenant and audit-friendly.
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 ai integration services in Riyadh, Saudi Arabia. AI inside the systems you already run. CRM, ERP, help desk, product. Book a scoping call.
SM Stratagem builds rag development in Kuwait City, Kuwait. Retrieval that finds the right doc. Grounded, cited generation. Ingestion pipeline you own.
SM Stratagem builds rag development in Doha, Qatar. 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.