Operations and business leaders
Hours saved on reading and drafting.
Before a model ships in Saudi Arabia
What PDPL and Arabic reality decide about model selection
A generative AI build for a Saudi enterprise is a model-selection decision and a data-residency decision at the same time. This is how we make both, together.
| Model route | Arabic capability | PDPL / residency fit | Where we use it |
|---|---|---|---|
| Arabic-tuned open model (e.g. Jais family) self-hosted | Strong MSA, Saudi dialect coverage limited to what it was tuned on | Runs fully in-country, no cross-border transfer | PDPL-sensitive documents, citizen or patient data |
| Frontier commercial model (GPT, Claude, Gemini) in a Saudi cloud region | Best general MSA reasoning, dialect handling improving but not native | Residency depends on the named region; contract must state it explicitly | General-purpose drafting and RAG where content is not restricted |
| Hybrid: Arabic-tuned model for sensitive fields, frontier model for the rest | Balances dialect accuracy with general reasoning | Restricted fields never leave the in-country boundary | Most Saudi enterprise deployments we ship |
Worked example
A Saudi enterprise needed contract summarization across Arabic and English procurement files, some containing supplier bank details subject to PDPL. We routed restricted fields through a self-hosted, Arabic-tuned model running in-country, and general summarization through a frontier model in a Saudi cloud region, with a single retrieval layer over both so the reviewer sees one answer, not two systems.
Our Expertise
Generative AI & LLMs
Enterprise-grade generative AI, from custom LLM fine-tuning and RAG pipelines to AI agents and content automation, transforming how organizations create, reason, and decide.
The service at a glance
The problem, our work, your result
- Business problem
Knowledge is buried in documents nobody has time to read.
Staff search, summarise and draft by hand, and public AI tools are not allowed near company data.
- What we do
We build secure generative AI on your documents for search, summaries and drafting, in natural Arabic and English, with sources shown.
- What you receive
- Search across your documents
- Summaries and drafts with sources
- Access rules respected
- Quality tested in Arabic
Who it is for
Built for the people who own the decision
Legal, HR and policy teams
Fast, sourced answers.
Risk, compliance and security
Data that stays private.
CDO, CIO and CTO
One secure platform for all.
The goal: an AI-ready organisation
Where this service moves you
Data readyThis service
Connected, clean, governed and allowed for AI use.
KPIs ready
Agreed measures, one definition each, a named owner.
Organisation readyThis service
Roles, decision rights and skills to adopt AI safely.
=AI that reaches production and creates value
Approach
How we work with you
- 01
Select
Pick documents and use case.
- 02
Prepare
Index content with access rules.
- 03
Build
Configure models and prompts.
- 04
Evaluate
Test answers for accuracy.
Engagement options
Start where you are
GenAI pilot
One team, real documents.
Enterprise rollout
Secure platform for many teams.
Managed GenAI
Ongoing quality and cost control.
Scope
What is in, what is not
Included
- Document indexing
- Search and summary assistant
- Access controls
- Arabic quality testing
Handled separately
- Public chatbot on open topics
- Training a model from scratch
Bilytica frameworks behind this service
Our own methods, not a generic playbook
SOVEREIGN
All Bilytica frameworksSovereign AI Framework
Residency, key custody, model routing and Arabic quality treated as architecture, not policy text.
See the frameworkTRUSTAI Assurance Framework
One control set that answers NIST, ISO 42001, the EU AI Act and the Saudi regulators at once.
See the frameworkGenerative AI has changed what software can do. But moving from ChatGPT demos to production-grade enterprise GenAI requires deep expertise in LLM selection, fine-tuning, retrieval-augmented generation (RAG), guardrails, and responsible AI. We build GenAI systems that are accurate, secure, and compliant, with Arabic-first capabilities that no global vendor can match. From document intelligence and code generation to AI agents that orchestrate complex business workflows.
What We Do
Generative AI & LLMs capabilities
- 01
Custom LLM Fine-Tuning
Fine-tune open-source and commercial LLMs on your proprietary data, contracts, policies, products, Arabic content, for domain-specific accuracy.
- 02
RAG & Knowledge Retrieval
Build retrieval-augmented generation pipelines that ground LLM responses in your enterprise knowledge base, no hallucinations, always cited.
- 03
AI Agent Orchestration
Multi-agent systems that plan, reason, and execute complex business workflows, from research to report generation to decision support.
- 04
Arabic GenAI
Arabic-first generative models for content creation, document summarization, translation, and conversational AI across all dialects.
- 05
Content & Code Generation
Automated report writing, marketing copy, email generation, code completion, and documentation, with human-in-the-loop review.
- 06
GenAI Guardrails & Safety
Prompt injection prevention, output filtering, bias detection, PII redaction, and responsible AI frameworks for enterprise deployment.
- 07
Document Intelligence
Extract, summarize, and analyze contracts, invoices, regulations, and medical records using multi-modal GenAI.
- 08
LLMOps & Model Management
Production ML pipelines for model versioning, A/B testing, monitoring, cost optimization, and continuous improvement.
- 09
Multi-Modal AI
Combine text, image, video, and audio understanding for richer enterprise AI, visual document processing, video summarization, and more.
Our Approach
From your data to a working result
- 01
Use Case Discovery
Identify highest-value GenAI use cases through workshops, ROI analysis, and feasibility assessment across business units.
- 02
Data & Knowledge Preparation
Curate, clean, and structure enterprise knowledge for RAG indexing and model fine-tuning.
- 03
Model Selection & Fine-Tuning
Select optimal base model (GPT-4, Claude, Llama, Gemini, Jais), fine-tune on domain data, and benchmark accuracy.
- 04
RAG Pipeline & Guardrails
Build retrieval pipeline with semantic search, re-ranking, citation, and deploy safety guardrails and output filters.
- 05
Integration & UX
Embed GenAI into existing workflows, CRM, ERP, support platforms, with intuitive user interfaces.
- 06
Monitor & Optimize
Production monitoring for accuracy, latency, cost, and user satisfaction. Continuous model updates and knowledge refresh.
Industry Applications
Where it makes a difference
Technology Stack
Technologies & Models We Use
Enterprise-grade tools and frameworks powering our solutions.
P
Python
Language
P
PyTorch
Deep Learning
T
TensorFlow
ML Framework
F
FastAPI
API Serving
R
Redis
Vector Cache
D
Docker
Deployment
K
Kubernetes
Orchestration
A
Azure
Cloud
FAQ
Questions buyers ask us
Straight answers before you commit
How do I choose the right generative AI development services provider?
Look for production deployments (not demos), domain expertise in your industry and language (Arabic + English for the GCC), and end-to-end ownership, strategy, data preparation, fine-tuning, RAG, guardrails, and LLMOps. Ask for measurable outcomes from past engagements (accuracy, latency, cost-per-query) rather than logos alone.
How much do generative AI development services cost?
Pilots typically range from $40K–$120K for a focused use case (e.g. one RAG application or document automation workflow). Production-grade enterprise rollouts with fine-tuned models, Arabic support, and integrations run $150K–$500K+ depending on data volume, accuracy targets, and infrastructure. We scope every engagement against ROI before quoting.
Do you offer end-to-end generative AI implementation?
Yes. We cover the full lifecycle: use-case discovery and ROI modeling, data and knowledge curation, model selection and fine-tuning (GPT-4, Claude, Llama, Gemini, Jais), RAG pipelines with semantic search and citation, guardrails for safety and compliance, system integration into CRM/ERP/support tooling, and LLMOps for production monitoring and continuous improvement.
Can you build Arabic-first generative AI?
Yes, and it's our core differentiator. Most global vendors fine-tune on English-dominant corpora and fail on MSA, dialects, and right-to-left rendering. We build Arabic-first GenAI using models like Jais alongside fine-tuned open-source LLMs, with retrieval pipelines that handle mixed Arabic/English knowledge bases and SAMA and PDPL compliance contexts.
How long does a generative AI development project take?
A focused pilot (one use case, one team, one data source) reaches production first. Multi-use-case enterprise rollouts follow, phased across data preparation, fine-tuning, integration, and change management, with the schedule agreed with you after scoping. We deliver in short sprints with working software at every checkpoint.
How do you prevent hallucinations and ensure accuracy?
It is layered: (1) RAG grounding, every answer is retrieved from your own knowledge base with explicit citations, (2) guardrails, prompt injection prevention, output filtering, PII redaction, and policy enforcement, (3) human-in-the-loop review for high-stakes outputs (legal, medical, financial). We benchmark accuracy at 95%+ on grounded queries before going live.
Which model do you use for Arabic-heavy Saudi deployments, and does it stay in Saudi Arabia?
We route by field sensitivity, not by preference. Content subject to PDPL (customer, citizen, patient data) runs on a self-hosted, Arabic-tuned model inside Saudi data borders; general drafting and non-restricted retrieval can use a frontier commercial model hosted in a named Saudi cloud region. Both sit behind one retrieval layer, so the split is invisible to the end user.
Ready to Explore Generative AI & LLMs?
15 minutes. One strategy call. A customized roadmap tailored to your business.