RAG & Enterprise Knowledge AI · UAE & Middle East
Turn your company's knowledge into intelligence
Wrexa builds retrieval-augmented generation (RAG) systems that answer questions from your own documents, wikis, tickets and databases — with citations, access controls and answers your team can verify.
remote-first — we work with teams across the UAE and the wider Middle East
- Staff waste time searching across drives, wikis and old emails
- Generic chatbots make things up about your policies and products
- Sensitive documents must stay private and permission-aware
- Support teams give inconsistent answers to the same question
What we build
RAG & knowledge AI: what you get
Knowledge ingestion pipelines
Connectors, parsing and chunking for PDFs, Office files, web pages, tickets and databases.
Semantic & hybrid search
Embeddings, vector databases, keyword search and re-ranking tuned on your queries.
Cited answers
Responses grounded in retrieved sources, with links back to the exact passage.
Permission-aware retrieval
Users only see answers drawn from documents they're allowed to access.
Retrieval evaluation
Measured recall and answer quality on real questions, not just demos.
Use cases
Where it pays off
- Enterprise
- Internal copilots for HR, IT and policy questions.
- Customer support
- Agent-assist and self-service answers from help-centre content.
- Legal & compliance
- Search across contracts and regulations with citations.
- Real estate
- Answers from property documents, disclosures and zoning rules.
How we build
From napkin sketch to production
01Discover
Business problem, AI feasibility, architecture.
02Data
Collection, cleaning, annotation, pipelines.
03Build
Models, agents, retrieval and the application.
04Evaluate
Accuracy, latency, cost and safety.
05Deploy
AWS, Azure, GCP, private cloud or edge.
06Operate
Monitoring, observability and optimisation.
Typical stack
FAQ
RAG & knowledge AI questions
What is RAG?
Retrieval-augmented generation finds the most relevant passages from your content and gives them to the language model, so answers are grounded in your data rather than the model's memory.
Does our data get used to train public models?
We design RAG so your documents stay in your storage and are sent only to model endpoints under agreements that don't train on your data — or to models you host yourself.
How do you stop hallucinations?
Grounding in retrieved sources, requiring citations, refusing when evidence is missing, and evaluating answers against a test set before launch.
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Serving clients across United Arab Emirates and Abu Dhabi and Sharjah and Saudi Arabia and Qatar and Oman and Bahrain and Kuwait and the United States and India.
your idea is the first frame —
Let's build your rag & knowledge ai project.
Share the problem, your data and your timeline. We'll reply with a practical path from idea to production — wherever you are in the UAE or the wider Middle East.