ZTABS builds RAG (Retrieval-Augmented Generation) systems for Riyadh enterprises — Arabic-English bilingual knowledge retrieval, government document intelligence for Saudi regulatory compliance, and enterprise search systems that understand the nuances of Arabic text, Saudi legal terminology, and domain-specific knowledge across Vision 2030 industries.
Saudi organizations sit on massive document repositories that nobody can search effectively. A Saudi ministry has 40 years of Arabic regulatory documents — Royal Decrees, ministerial decisions, circulars, and implementation guidelines — stored in document management systems or, worse, in filing cabinets that were scanned to PDF without OCR. A Saudi bank has compliance manuals, SAMA circulars, internal policies, and customer-facing documentation across Arabic and English, and a compliance officer searching for guidance on a specific regulation must manually search through 15 document sources. A Saudi hospital network has 12 years of clinical protocols, pharmacy guidelines, and administrative policies that staff search by asking the most senior colleague who might remember where the relevant document lives. Standard search engines fail these organizations for three reasons. First, Arabic text search is fundamentally harder than English search. Arabic is morphologically rich — the root system means a single concept can appear in dozens of surface forms. The word for "regulation" (تنظيم, tanzeem) shares a root with "organization" (منظمة, munazzama) and "organized" (منظم, munazzam), and a useful search system must understand these relationships. Arabic diacritics (tashkeel) are rarely written in modern documents but change meaning — a system that treats diacritical variants as different words will miss relevant results. And Saudi government Arabic uses formal (MSA) vocabulary that differs from the Egyptian or Levantine Arabic that many NLP tools are trained on. Second, Saudi organizations operate bilingually. Contracts may be in Arabic with English technical appendices. Emails mix languages mid-sentence. A useful knowledge system must retrieve relevant content regardless of whether the query is in Arabic or English, and regardless of the language of the source document. Third, the documents that matter most — regulatory guidance, legal opinions, compliance interpretations — require contextual understanding, not keyword matching. When a compliance officer asks "can we offer this product to non-resident customers?" the answer is spread across 3 SAMA circulars, 2 CMA regulations, and an internal legal opinion from 2019. RAG solves this by combining retrieval (finding the relevant document fragments) with generation (synthesizing a coherent answer from multiple sources, with citations).
Riyadh's RAG demand is driven by Vision 2030's digitization mandates, the volume of Arabic regulatory content, and the growth of knowledge-intensive industries. Government: 150+ government entities need to make decades of Arabic regulatory content searchable and actionable. The Saudi Data & AI Authority (SDAIA) promotes AI adoption across government, and RAG is a natural first AI application because it improves productivity without changing business processes. The Royal Court (Diwan), the Bureau of Experts (Hayat al-Khubara'), and the Saudi Human Rights Commission all manage extensive Arabic document archives. Legal: Saudi Arabia's legal system is undergoing codification. The Personal Status Law (2022), Evidence Law (2022), and Penal Code for Discretionary Sanctions (2024) are recent — lawyers need systems that can retrieve relevant articles, precedents, and regulatory guidance across the old and new legal frameworks simultaneously. The Sharia court system, Board of Grievances (Diwan al-Mazalim), and commercial courts each have distinct procedural and substantive rules that practitioners must navigate. Healthcare: Saudi hospitals produce clinical guidelines, pharmacy formularies, and administrative policies in Arabic. The Saudi Commission for Health Specialties (SCFHS) maintains medical standards. Hospital networks like KFMC, KFSH&RC, and the MOH hospital system need knowledge retrieval for clinical protocols. Financial services: SAMA, CMA, and the insurance regulator (SAMA for insurance supervision) collectively produce thousands of pages of Arabic regulations annually. Compliance teams at Saudi banks spend 30-40% of their time searching for and interpreting regulatory guidance. Oil & gas: Saudi Aramco's technical knowledge base — engineering standards, safety procedures, maintenance protocols — is one of the world's largest industrial knowledge repositories. Aramco's Investment Management Area (IMA) and emerging ventures need knowledge systems that span decades of operational documentation.
Our Riyadh RAG development addresses the Arabic-specific challenges that make Middle Eastern knowledge retrieval distinct from English-language RAG systems. Arabic NLP pipeline: (1) Document ingestion with Arabic OCR for scanned documents (Tesseract with Arabic training data, or ABBYY FineReader for high-accuracy Arabic OCR). Saudi government PDFs often contain scanned images of printed Arabic text — the OCR step is critical and error rates for Arabic are higher than for Latin scripts. We implement OCR correction pipelines with Arabic spell-checking and manual review for high-value documents. (2) Arabic text preprocessing: normalization (handling alef variants: أ, إ, آ → ا; handling taa marbuta: ة → ه for matching; removing diacritics for search while preserving them for display), tokenization (Arabic-specific tokenizers like CAMeL Tools rather than generic WordPiece), and morphological analysis (lemmatization using Arabic morphological analyzers to reduce surface form variations to root forms for more effective retrieval). (3) Bilingual embedding: we use multilingual embedding models (e.g., multilingual-e5-large, Arabic-specific models from AraGPT, or fine-tuned models trained on Saudi domain vocabulary) that map Arabic and English text into the same vector space — so an English query retrieves relevant Arabic documents and vice versa. For domain-specific terminology (Saudi legal terms, medical Arabic, oil & gas technical vocabulary), we fine-tune embedding models on the client's own corpus to ensure domain terms are properly represented. Retrieval architecture: (1) Hybrid retrieval combining semantic search (vector similarity in the embedding space) with keyword search (BM25 with Arabic-aware tokenization). Semantic search handles the "what does this regulation mean?" queries. Keyword search handles the "find circular 2023-47" exact-reference queries. We weight the combination based on query type. (2) Chunking strategy tailored to Arabic document structure: Saudi regulatory documents have specific formatting conventions (numbered articles, sub-articles with Arabic letters, footnotes with Hijri dates). Our chunking preserves these structural boundaries rather than splitting at arbitrary character counts. (3) Metadata-enhanced retrieval: documents are tagged with issuing authority, date (Gregorian and Hijri), document type, topic classification, and applicable entities — enabling filtered retrieval ("show me SAMA circulars from 2023 about digital banking"). Generation with citations: (1) We prompt the LLM (GPT-4, Claude, or Jais — the Arabic-specific LLM developed by G42 in UAE) to generate answers grounded in the retrieved documents, with inline citations to specific document sections. (2) Arabic answer generation requires LLMs with strong Arabic capability — not all models handle formal Arabic (MSA) well, especially for technical and legal content. We evaluate model performance on the client's specific domain before selection. (3) Hallucination prevention: we implement citation verification — every claim in the generated answer must trace to a retrieved document chunk. Claims without supporting evidence are flagged or removed.
Banking / Financial Services
A Saudi bank's compliance department managed regulatory obligations across SAMA banking regulations, CMA securities rules, anti-money laundering regulations, and internal compliance policies — totaling approximately 8,500 documents in Arabic and English accumulated over 18 years. Compliance officers spent an average of 45 minutes per regulatory inquiry searching through 5 different document sources: the SAMA website (for current circulars), an internal SharePoint library (for historical regulations), a Lotus Notes database (legacy — containing compliance interpretations from 2008-2016), email archives (for informal SAMA guidance), and printed binders (for pre-2010 regulations that were never digitized). The bank faced three operational problems: (1) Compliance response time: 45 minutes average per inquiry meant the team of 8 compliance officers could handle approximately 50 inquiries per day — insufficient during regulatory examination preparation when inquiry volume spikes to 200+ per day. (2) Inconsistent interpretation: different compliance officers, searching different sources, sometimes provided contradictory guidance on the same regulatory question. (3) Institutional knowledge risk: 2 senior compliance officers (combined 35 years of experience) were approaching retirement — their departure would take irreplaceable knowledge of historical regulatory context and interpretation precedents.
We built a bilingual Arabic-English RAG system over 14 weeks: (1) Document ingestion: digitized 1,200 pre-2010 documents using Arabic OCR with manual correction (Arabic OCR accuracy: 89% raw, improved to 97% after correction pipeline). Ingested 7,300 digital documents from SharePoint, Lotus Notes, and the SAMA website archive. Total corpus: 8,500 documents, 2.3 million Arabic words, 800,000 English words. (2) Arabic processing pipeline: normalized Arabic text (alef/taa marbuta normalization, diacritics removal for indexing), applied Arabic morphological analysis for improved retrieval (lemmatizing regulatory terms to their root forms), and tagged documents with metadata (issuing authority, circular number, date in both Gregorian and Hijri calendars, topic classification using a taxonomy aligned with the bank's compliance structure). (3) Bilingual vector embeddings: used a fine-tuned multilingual embedding model trained on 500 manually curated query-document pairs from the bank's historical compliance inquiries. The fine-tuning improved retrieval accuracy for Saudi regulatory terminology by 23% over the base model. (4) Hybrid retrieval: combined vector similarity search with BM25 keyword search, weighted 60/40 for semantic vs. keyword. Added metadata filters for date range, issuing authority, and topic. (5) Generation layer: Claude with Arabic system prompts optimized for Saudi regulatory language. Every generated answer includes numbered citations to specific document sections. The system presents the cited document passages alongside the generated answer so compliance officers can verify the interpretation. (6) Knowledge capture: conducted 12 sessions with the 2 senior compliance officers, recording their interpretations and reasoning on 150 common regulatory scenarios. These interpretations were added to the knowledge base as "compliance precedent" documents — preserving institutional knowledge.
Average compliance inquiry response time: 45 minutes → 4 minutes (91% reduction). The system retrieves relevant regulatory passages and generates a draft answer with citations in under 30 seconds; the compliance officer reviews and validates in 3-4 minutes. Daily inquiry capacity: 50 → 300+ (limited by team size, not system capacity). Consistency: the same regulatory question, asked by different compliance officers, now retrieves the same source documents and generates consistent interpretations. SAMA examination preparation (which previously required 3 weeks of intensive document gathering) was completed in 4 days using the RAG system to retrieve and organize relevant evidence. The retirement of one senior compliance officer in month 6 after deployment validated the knowledge capture approach — their documented interpretations continued serving the team through the RAG system. The system handles 85% of inquiries without escalation to a senior officer (up from 45% before deployment). Cost: SAR 420,000 for development, SAR 18,000/month for hosting and LLM inference. Annual compliance team productivity gain valued at SAR 1.2 million (equivalent of 3 additional compliance officers at SAR 400,000/year loaded cost).
Ingest, chunk, embed, and index your documents for fast, accurate retrieval with any LLM.
Internal knowledge systems that let employees search across wikis, SOPs, contracts, and Slack history.
Give your customers an AI assistant that answers product questions using your documentation and help center.
Pull data from PDFs, web pages, databases, APIs, Google Drive, Notion, Confluence, and more.
Every answer includes source citations so users can verify and trust the information.
Continuously improve retrieval quality with evaluation frameworks, feedback loops, and reranking.
Arabic RAG development in Riyadh is an emerging field. Global RAG providers (Glean, Coveo, Guru) support Arabic poorly — their retrieval systems are optimized for English with Arabic as an afterthought. Saudi AI companies (Mozn, Lucidya, Elm) offer some Arabic NLP capabilities but are not focused on RAG specifically. Systems integrators (Deloitte, Accenture, IBM) can build RAG systems but rely on English-first frameworks adapted for Arabic. The gap: no established provider specializes in Arabic-first RAG with the depth of Arabic NLP processing (morphological analysis, bilingual embedding, Arabic OCR correction) that Saudi enterprise documents require. ZTABS builds Arabic-native RAG systems from the text processing layer up — not English systems with Arabic bolted on.
RAG development in Riyadh ranges from SAR 200,000 ($53,000) for focused knowledge retrieval systems (single document corpus, Arabic-only, limited scope) to SAR 600,000+ ($160,000+) for enterprise bilingual RAG platforms with OCR pipeline, custom embeddings, and multi-source integration. The bank's system (8,500 documents, bilingual, custom embeddings, knowledge capture): SAR 420,000 development + SAR 18,000/month hosting. Annual productivity value: SAR 1.2 million — a 2.8:1 first-year ROI including development costs.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract — we ship and operate our own software. 17 products in production.
Common questions about RAG & knowledge systems for Riyadh businesses
Yes — and this is the default for Saudi business and regulatory documents. Modern Standard Arabic in government and business contexts rarely includes diacritics (tashkeel). Our Arabic processing pipeline normalizes text with and without diacritics into a consistent format for embedding and retrieval. The system handles the ambiguity that diacritic-less Arabic creates (where the same consonant skeleton can represent different words) through contextual embedding — the surrounding words disambiguate the meaning, and this context is captured in the vector representation.
ZTABS is a remote-first RAG & knowledge systems agency serving Houston businesses — including custom rag pipelines, enterprise knowledge bases, customer-facing ai search. We work with Energy & Oil/Gas, Healthcare & Biotech, Aerospace & Defense companies in Houston, TX via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
RAG & Knowledge Systems in New York, NYZTABS is a remote-first RAG & knowledge systems agency serving New York businesses — including custom rag pipelines, enterprise knowledge bases, customer-facing ai search. We work with Finance & Fintech, Media & Advertising, Fashion & Retail companies in New York, NY via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
RAG & Knowledge Systems in Los Angeles, CAZTABS is a remote-first RAG & knowledge systems agency serving Los Angeles businesses — including custom rag pipelines, enterprise knowledge bases, customer-facing ai search. We work with Entertainment & Media, E-commerce & DTC Brands, Gaming & AR/VR companies in Los Angeles, CA via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Development in Riyadh, Saudi ArabiaZTABS is a remote-first web development agency serving Riyadh businesses — including full-stack development, progressive web apps, api development. We work with GovTech, FinTech, Energy Tech companies in Riyadh, Saudi Arabia via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
Web Design in Riyadh, Saudi ArabiaZTABS is a remote-first web design agency serving Riyadh businesses — including ui/ux design, responsive design, custom interfaces. We work with GovTech, FinTech, Energy Tech companies in Riyadh, Saudi Arabia via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
AI Development in Riyadh, Saudi ArabiaZTABS is a remote-first AI development agency serving Riyadh businesses — including llm integration & fine-tuning, ai agents & automation, rag & knowledge systems. We work with GovTech, FinTech, Energy Tech companies in Riyadh, Saudi Arabia via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
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