For operators in Doha, we run rag development projects that leave you with production systems your team can maintain, not a vendor-only black box.
RAG Development in Doha is what we do when a team is done running pilots and wants a system that actually ships. The buyers we work with in Doha tend to sit inside energy, finance, and sports infrastructure, and they want ROI they can point to at a board meeting. That means retrieval-augmented generation that actually retrieves the right thing before it generates, with clear ownership of what runs in production and who fixes it when something breaks. 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 Qatar 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 rag development project is the handover: docs, evals, runbook, and a person who picks up the phone. 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.
In Doha, we usually enter through ministries, QIA-backed operators, and QFC-registered firms. The gap is rarely the model — it's the data plumbing and the handover to operations. We spend the first two weeks mapping both, then we build.
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 QatarEnergy contractors, QFC-registered firms, and ministries in Doha, we deliver RAG systems inside existing vendor frameworks and procurement rules. Compliance is a delivery input, not a surprise at UAT.
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. Public-sector and energy-adjacent work in Doha typically requires sitting inside an existing vendor framework and delivering under strict procurement rules. That's the shape we default to. We don't take POC-only work in Qatar — it wastes everyone's time — so we scope for production from the start.
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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.