RAG Development in Abu Dhabi, done the way it should be: scoped small, measured on real usage, and handed over with docs and runbooks your engineers can read.
For Abu Dhabi companies, we treat rag development as engineering — versioned, tested, monitored — not as a science project you renew every year. The buyers we work with in Abu Dhabi tend to sit inside energy, government, and finance, 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. 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 United Arab Emirates, and we know which cloud regions actually keep data on-shore. 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're comparing agencies, ask us how we measure success before we quote — that's usually the fastest way to see who's serious.
The Abu Dhabi 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 entities inside ADGM and the Abu Dhabi government, we build RAG systems that respects data-residency, vendor-review, and procurement rules from the SoW onward. The cloud region and audit trail get decided before the first line of code.
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. We deliver into ADGM-registered entities and Abu Dhabi government departments on a regular basis, which usually means tighter data-residency and vendor-review controls. We plan for those constraints inside the SoW rather than trying to bolt them on right before go-live, so audits and reviews rarely become the bottleneck.
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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.