Arabic Chatbots and Private AI from an AI Company in Saudi Arabia

Amwaj Solutions is an AI company in Saudi Arabia that designs, builds and runs private AI systems — Arabic chatbots, internal knowledge assistants and AI agents — on infrastructure you control, on-premise or in a sovereign cloud inside the Kingdom. Your data stays in Saudi Arabia, your assistants understand Saudi Arabic, and your costs stay predictable at production volume.

We are an engineering partner, not a reseller of someone else's chatbot. We select the model, build the data pipelines, integrate with your systems and operate the platform, so AI becomes a dependable part of how your organisation serves customers and makes decisions.

What does our Arabic chatbot development and private AI service include?

• Arabic customer-service chatbots for your website, mobile app and WhatsApp Business, fluent in Modern Standard Arabic, Saudi dialect and English.
• Internal knowledge assistants that answer staff questions from your own policies, contracts, manuals and reports, with a citation for every answer.
• AI agents that take action — creating tickets, checking order status, drafting replies or updating records — under clear permissions and human approval.
• Private LLM infrastructure: model selection, Arabic fine-tuning, inference servers, monitoring and ongoing optimisation.

Private AI vs public AI APIs: which is right for your organisation?

Public AI APIs are the fastest way to experiment. Private (sovereign) AI is usually the better fit once personal, financial or government data is involved, or once usage reaches enterprise volume. The trade-offs look like this:

Factor	Public AI API	Private / sovereign AI
Where data is processed	Provider's data centres, often outside the Kingdom	Your servers or a cloud region inside Saudi Arabia
PDPL cross-border transfers	Needs a lawful transfer basis and a risk assessment	Personal data can stay in the Kingdom by design
Cost model	Per-token or per-request fees that grow with usage	Upfront infrastructure, then largely fixed running costs
Customisation	Prompting and limited fine-tuning	Full control: fine-tuning, retrieval, guardrails, logging
Vendor dependency	Model changes, price changes and outages are outside your control	You decide when to upgrade or switch models
Time to first prototype	Days	Weeks, depending on hardware and data readiness
Best for	Low-risk, low-volume experiments with non-sensitive data	Customer data, internal documents, regulated and government workloads

Many organisations end up with a hybrid: public models for non-sensitive tasks such as marketing drafts, private models wherever personal or confidential data flows.

How do Arabic chatbots handle customer service in Saudi Arabia?

Website and app chat

A modern Arabic chatbot does not follow a rigid decision tree. It understands the question, retrieves the right answer from your approved content, and connects to your CRM, booking or order systems to give a specific reply — "your order shipped yesterday", not "please contact us". When it is not confident, it hands over to a human agent with the full conversation attached.

WhatsApp Business API

WhatsApp is where many Saudi customers already talk to businesses, so we connect chatbots through the WhatsApp Business Platform with approved message templates, opt-in handling and live-agent handover. Note that Meta's updated Business API terms, in force since 15 January 2026, bar general-purpose AI assistants from the platform; business-specific bots for support, bookings and order updates remain permitted, and that is exactly what we build.

Saudi dialect, code-switching and Arabizi

Customers write "ابي اعرف وين طلبي" rather than textbook Arabic, mix in English terms and sometimes type Arabic in Latin letters. We test models on real Saudi phrasing, normalise spelling variations, and build evaluation sets from your own (anonymised) conversations before go-live. Our Arabic chatbot guide for Saudi Arabia explains these choices in more detail.

What is an internal knowledge assistant?

An internal knowledge assistant uses retrieval-augmented generation (RAG): instead of relying on what the model memorised, it searches your approved documents, passes the relevant passages to the model and answers with references. This keeps answers current and auditable. We build RAG systems that respect existing access rights (a finance analyst does not see HR files), read scanned Arabic PDFs, and log every query for review.

How do AI agents automate work?

AI agents go a step further than chat: they call tools and systems to complete a task — classifying an incoming request, pulling data from the ERP, drafting a response and routing it for approval. We design agents with narrow permissions, audit trails and a human sign-off on any step with financial or legal impact. For rule-based, high-volume processes, agents work alongside our RPA and intelligent automation services.

Which Arabic LLMs can run privately in Saudi Arabia?

Several capable models can be hosted on your own infrastructure. The right choice depends on your Arabic workload, hardware and licence requirements:

• ALLaM — the Arabic–English model family developed by the National Center for AI at SDAIA. ALLaM-7B-Instruct is published as open weights on Hugging Face, trained on 4 trillion English tokens followed by 1.2 trillion mixed Arabic/English tokens. In 2025, the PIF-backed company HUMAIN took over ALLaM development and launched its ALLaM 34B model in August 2025. See our ALLaM business guide.
• Jais — an Arabic-first open model released in August 2023 by Inception (G42), MBZUAI and Cerebras; the original 13-billion-parameter version was trained on 116 billion Arabic and 279 billion English tokens.
• Qwen3 — Alibaba's multilingual family, released in 2025, supports 119 languages and dialects, with its dense models under the Apache 2.0 licence.
• Llama 4 — Meta's model lists Arabic among its 12 officially supported languages, under Meta's own licence.

Benchmarks only go so far. We shortlist two or three models and score them on your real Arabic and English tasks before committing; our guide to running Arabic LLMs on-premise covers the hardware side.

How do PDPL and SDAIA's AI Ethics Principles affect an AI project?

Saudi Arabia's Personal Data Protection Law (PDPL), issued by Royal Decree M/19 in September 2021, came into force on 14 September 2023, with a one-year transition period that ended in September 2024. SDAIA is the competent authority. The law limits transfers of personal data outside the Kingdom to specified purposes and approved mechanisms, and fines can reach SAR 5 million per violation, doubled for repeat offences.

SDAIA also adopted its AI Ethics Principles in September 2023: fairness; privacy and security; humanity; social and environmental benefits; reliability and safety; transparency and explainability; and accountability and responsibility. They take a risk-based approach, with stricter expectations for higher-risk systems. In practice we build in data minimisation, role-based access, conversation logging with retention limits, clear disclosure that users are talking to AI, and human escalation — alongside security controls aligned with the NCA Essential Cybersecurity Controls.

Deployment options: on-premise, sovereign cloud or hybrid

• On-premise: GPU servers in your own data centre. Maximum control; the usual choice for government and classified data.
• Sovereign cloud in KSA: dedicated GPU capacity in a cloud region inside the Kingdom. Faster to start, no hardware to manage, data stays resident.
• Hybrid: sensitive inference on-premise, burst or non-sensitive workloads in a Saudi cloud region.

How does a private AI project run?

1. Use-case discovery: we rank candidate use cases by value, data readiness and risk, and agree measurable success criteria.
2. Architecture and data assessment: deployment model, model shortlist, integrations and security requirements.
3. Pilot: one use case, real users, real data, measured against the success criteria.
4. Production: hardening, monitoring, access controls, load testing and staff training.
5. Operate and improve: performance reporting, model updates and new use cases on the same platform.

Proven in a government environment

For a Saudi government entity, we delivered fully on-premise AI inference with internal data pipelines, role-based access control and Saudi data residency. The result: 100% independence from external AI APIs, full data residency and predictable cost at enterprise volume. Read the private AI government case study.

If you are weighing an Arabic chatbot, an internal assistant or a move away from public AI APIs, request a consultation. We respond within 24–48 business hours with clear next steps.