Retrieval-augmented generation engineered for Arabic: the system retrieves passages from your Arabic corpus and generates an answer strictly from them, with citations.

Why not use a general model?

A general model has not read your documents and cannot cite them. RAG grounds the answer in your own material.

Does it handle mixed Arabic and English documents?

Yes. A single index serves both languages, with normalisation for diacritic and spelling variance.

Does it respect document permissions?

Yes. Retrieval is filtered by the requesting user's access rights in the source system.

How do you measure quality?

With Qiyas: groundedness, citation validity and task success, scored separately per language.

Give us a corpus sample and twenty real questions. We build a scoped pilot and report measured results.

Service

Arabic RAG systems

Answers grounded in your Arabic documents, with citations

Siyada Tech builds Arabic retrieval-augmented generation systems: search and answer layers over an organisation's own Arabic and English documents, where every answer carries the citation it came from. We handle Arabic segmentation, dialect and diacritic variance, and deploy inside the customer's environment.

Last reviewed: 2026-08-08 — Siyada Tech engineering

What it is

An engineered retrieval pipeline — parsing, Arabic-aware chunking, hybrid lexical and semantic retrieval, reranking, grounded generation and citation rendering — built for a specific corpus and a specific set of questions.

The problem it solves

Generic RAG built for English degrades on Arabic: tokenisation splits words badly, diacritics and spelling variants break lexical match, and mixed Arabic-English documents retrieve inconsistently. The result is confident answers from the wrong passage.

Who it is for

Organisations with large Arabic document estates
Knowledge, legal, policy and customer-support functions
Government entities publishing Arabic regulation and guidance

How it works

01Parse the corpus, including scanned material where OCR quality allows.
02Chunk with Arabic-aware segmentation that respects sentence and clause structure.
03Index hybrid: lexical for exact terminology, semantic for paraphrase, normalised for diacritics.
04Rerank retrieved candidates before generation.
05Generate strictly from retrieved context and render inline citations.
06Evaluate groundedness and citation validity per language with Qiyas.

Deployment

In-tenant deployment; the index and corpus stay in the customer's environment
Read-only connectors to SharePoint, file shares and document management systems
Permission-aware retrieval that respects source-system access rights
API and web interface, SSO via the customer's identity provider

Security & compliance

No corpus or index leaves the tenant
PDPL and NDMO-aligned handling of personal data in documents
Retrieval filtered by the requesting user's document permissions
Audit log of question, retrieved passages and generated answer

Evidence

We publish a metric only with its definition, method, sample size and evaluation date. Anything not yet measured to that standard is marked below rather than claimed.

Metric	Definition	Method	Sample size	Evaluated	Limitations
Time from kickoff to production	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]
Task success rate in production	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]
Human-review escalation rate	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]	[EVIDENCE REQUIRED]

Limitations & what it does not do

Retrieval cannot recover information that is not in the corpus.
Poor OCR on scanned Arabic materially reduces retrieval quality.
Grounding reduces but does not eliminate unsupported statements; we measure rather than promise zero.

Frequently asked questions

Pilot Arabic RAG on your corpus

A corpus sample and twenty real questions is all we need to produce a measured pilot.