Arabic-first · UAE and GCC · US and Europe
AI systems that understand your knowledge, reason across your data, and act on your behalf.
VoraX builds the layer beneath enterprise AI: structure-aware document understanding, graph reasoning and agents that return answers traceable to the source, in Arabic and English as one system.
- Knowledge Intelligence
- GraphRAG
- Agentic AI
- Decision Intelligence
VoraX in two minutes
See all five products at work.
One platform, five products, and the evidence behind every answer. Narrated, with captions, so it works with the sound off.
Capabilities
Four layers, one system.
Structured tables and unstructured documents resolved into a single knowledge surface your people and your agents can both reason over.
- 01
Knowledge Intelligence
Contracts, policies, drawings, tickets and databases become one queryable body of organisational knowledge.
- Structure-aware parsing
- Entity and relation extraction
- Structured + unstructured together
- 02
GraphRAG
A knowledge graph next to a vector index, so retrieval follows real relationships instead of guessing from similarity alone.
- Hybrid retrieval
- Graph traversal
- Text-to-Cypher analytics
- 03
Agentic AI
Agents that plan, retrieve, verify and then act inside your systems, with every step written down.
- Plan → retrieve → verify
- Tool and system actions
- Full execution traces
- 04
Decision Intelligence
Answers arrive with their evidence attached: the document, the page, the path through the graph.
- Page-level citations
- Access control per document
- Audit logs on every query
The VoraX Brain
How a question becomes a grounded answer.
Not a black box. Five stages, each one inspectable. That is what makes the output defensible to a regulator, a board or a student.
Illustrative architecture of the VoraX pipeline.
Every source, as it actually is
Arabic and English PDFs, spreadsheets, databases, ticket systems. Reading order, headings and tables recovered from the document geometry rather than flattened into text.
Structure before meaning
Documents are decomposed into sections, clauses and tables; entities and the relationships between them are extracted and typed.
A knowledge graph and a vector index
Meaning is stored twice, as a graph of typed relationships and as dense vectors, so retrieval can follow connections and similarity at once.
An agent that shows its work
The agent plans a route through the graph, gathers evidence, checks it against the source and retries when the evidence does not hold.
An answer you can defend
The response carries its citations, its retrieval path and its permissions. Where you allow it, the agent goes on to act in your systems.
Arabic + English
Arabic is not a translation layer.
Most enterprise AI treats Arabic as English with the text reversed. Right-to-left layout, mixed-script tables and Arabic legal phrasing break that assumption on the first real document.
ما هي شروط القبول في برنامج هندسة الذكاء الاصطناعي؟
Admission requires a recognised secondary certificate with a minimum overall average of 85%, plus a passing score on the mathematics placement test.
Admission Policy, page 12
Asked in Arabic, answered from the organisation’s own policy document, not from general internet knowledge. Sample content, shown to illustrate the retrieval behaviour.
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Geometry-first RTL parsing
Reading order reconstructed from glyph positions on the page, so bilingual tables and columned Arabic policies survive ingestion intact.
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One bilingual knowledge base
A question in Arabic retrieves the English contract clause that answers it. Two languages, one index, not two disconnected systems.
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Built for the region
Designed around UAE and Saudi enterprise realities: bilingual governance documents, mixed-script records and public-sector review. Equally suited to teams in the US and Europe whose work involves Arabic.
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Deployed where your data lives
Runs inside your own environment when residency, procurement or data-control requirements demand it.
Leadership
Built by someone who has run this inside large organisations.
Umer Javaid
Founder & CEO, VoraX
- Ex-Emaar
- Ex-Digital Dubai
- AI Architect
Umer Javaid founded VoraX to build the layer that enterprise AI keeps skipping: understanding the organisation’s own documents and data well enough to answer from them. His work spans large-scale enterprise and public-sector technology in Dubai, and the open-source retrieval and document systems that VoraX runs on.
Contact
Tell us what a reliable answer has to look like.
Bring the documents your current system gets wrong. That conversation is usually more useful than a demo.
umerjavaid@vorax.systems- Focus
- Knowledge intelligence · GraphRAG · Agentic AI
- Region
- UAE · GCC · US · Europe
- Languages
- Arabic · English