We ship rag development projects in Manama for teams that need working software this quarter, not a strategy deck for next.
In Manama, we run rag development projects for operators who care about outcomes over demos and evaluation over adjectives. The buyers we work with in Manama tend to sit inside fintech, banking, and telecoms, and they want ROI they can point to at a board meeting. So our default is retrieval-augmented generation that actually retrieves the right thing before it generates, measured and iterated before anything touches production traffic. 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 Bahrain, 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 you already know the outcome you want, we can scope the first release inside a week and start building the week after.
The Manama projects that succeed have one thing in common: someone senior owns the outcome. We bring the engineering, the evals, and the on-call rota, but a business owner on your side is non-negotiable.
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 CBB-regulated fintechs and banks in Manama, our default RAG systems deployment passes vendor-management review and audit trail requirements out of the box. Regulatory posture drives the architecture, not the other way round.
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 CBB-regulated fintechs and banks in Manama, our default deployment shape passes standard vendor-management review and audit-trail requirements. We've been through the process enough times to know what will be asked, so we prepare the evidence early rather than during the review itself.
SM Stratagem builds llm development in Manama, Bahrain. LLM apps that survive production. Cost and latency instrumented. Prompts versioned and evaluated.
SM Stratagem builds ai data engineering in Manama, Bahrain. Data pipelines that don't rot. Quality measured, not assumed. Vector and structured, both.
SM Stratagem builds computer vision development in Manama, Bahrain. Vision for real environments. Edge or cloud, your call. Handles lighting and drift.
SM Stratagem builds rag development in Sharjah, United Arab Emirates. Retrieval that finds the right doc. Grounded, cited generation. Book a scoping call.
SM Stratagem builds rag development in Riyadh, 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.