Artificial Intelligence · Generative AI
Generative AI solutions in Saudi Arabia: RAG assistants, copilots and private LLMs on your data
GoAgile builds generative AI that answers from your own documents and systems: RAG pipelines, enterprise copilots, document intelligence and private models on your own servers, with guardrails and local data control.
- RAG pipelines configured on your documents and systems
- Private and on-premises LLM deployment
- Guardrails, evaluation and audit built in
- Grounded answers
- RAGGrounded answers
- Private LLMs
- On-premPrivate LLMs
- Arabic-capable
- AR + ENArabic-capable
- Every answer
- CitedEvery answer
What it is
What is generative AI for business, and what is RAG?
Generative AI creates text, summaries, code and answers from a prompt. For business use it needs your facts, which is what retrieval-augmented generation (RAG) provides: the system finds the relevant passages in your documents and data, gives them to the model, and returns an answer with citations. RAG keeps answers current and grounded without retraining a model on your data.
Most enterprise value comes from a few patterns: a knowledge assistant over policies and procedures, a copilot inside a business application, document summarisation and extraction, and drafting with human review. Each needs retrieval quality, access control and evaluation more than it needs a bigger model.
Where data cannot leave your environment, we deploy open models such as Llama, Mistral or Qwen on your own GPUs. Where it can, we use hosted models with masking and contractual controls. We help you choose, with the trade-offs written down.
Key facts
- Patterns
- RAG, copilots, summarisation and extraction
- Models
- Cloud APIs or open models on your servers
- Data
- Stays in your environment
- Quality
- Evaluated on your own questions
- Controls
- PII masking, guardrails and audit
What we build
From a RAG pipeline to a private model
Six workstreams. Most projects begin with RAG and add the rest as use grows.
Assistants that answer from your documents
We configure the full retrieval pipeline: ingest, chunk, embed, store, search, re-rank and cite. Retrieval quality is measured on your own questions, and access is checked for every user.
- Ingestion from PDF, Word, SharePoint, file shares and ERP data
- Chunking, metadata and embeddings
- Vector stores such as pgvector and Qdrant
- Hybrid search and re-ranking
- Permission-aware retrieval
- Citations and freshness checks
What you get
Answers grounded in your documents, with sources a reviewer can open.
Copilots inside the tools your teams already use
Internal assistants for HR, finance, IT, sales and operations, and copilots inside Microsoft 365 and Dynamics 365, built on your knowledge and respecting your permissions.
- Knowledge assistants for HR, finance, IT and sales
- Microsoft 365 Copilot and Copilot Studio extensions
- Azure OpenAI Service solutions
- SharePoint and Teams knowledge
- Copilots inside ERP workflows
- Usage and cost reporting
What you get
Staff get answers and drafts in the place they work.
Read, summarise and extract at scale
Summaries, extraction, classification and review of contracts, policies, reports and correspondence, in Arabic and English, with a person approving what matters.
- Summaries of long reports and contracts
- Field extraction and classification
- Arabic and English OCR and reading
- Policy and contract clause review
- Report and email drafting for review
- Links from every result to its source
What you get
Hours of reading reduced to minutes, with the evidence attached.
Models that run on your servers
When data must stay in your environment, we deploy open models on your own hardware: we size the GPUs, serve the model, secure it and run it, so nothing is sent to an outside provider.
- Open models such as Llama, Mistral, Qwen and Gemma
- Arabic-capable models, including ALLaM and Jais where they fit
- Serving with vLLM or Ollama on NVIDIA GPUs
- Quantisation and sizing for cost and speed
- Kubernetes deployment and air-gapped options
- A cost comparison with cloud APIs
What you get
Generative AI with no data leaving your control.
Customise a model only when it earns its keep
RAG handles most knowledge needs. Fine-tuning helps with tone, format and domain behaviour. We test both on your content, in Arabic and English, before recommending either.
- RAG versus fine-tuning decision
- Arabic and English evaluation sets
- Domain terminology and style
- LoRA and parameter-efficient tuning
- Dialect handling where needed
- Versioning and rollback of tuned models
What you get
The cheapest approach that meets your quality bar.
Quality you can measure, risks you can control
We measure groundedness, accuracy and refusal behaviour on your own cases, and put guardrails around inputs and outputs so the assistant stays inside policy.
- Test sets and groundedness checks
- Citation and hallucination checks
- Personal data masking
- Prompt-injection defences
- Content and policy filters
- Cost, latency and quality monitoring
What you get
An assistant you can put in front of staff or customers with confidence.
How we work
From first question to a live assistant
We start from the questions people actually ask and work backwards to the data and the model.
- 1
Discover
Use case, users, data sources, risks and success measures, agreed with the business owner.
- 2
Prepare
Documents cleaned, structured and permissioned, with a plan for keeping them current.
- 3
Build
The retrieval pipeline, prompts and interface, with guardrails from the first version.
- 4
Evaluate
Real questions, scored by reviewers, until quality meets the agreed bar.
- 5
Launch
A pilot group, feedback loops and monitoring, then wider rollout.
- 6
Operate
Quality, cost and data freshness reviewed regularly, and the model updated as needed.
Reference
RAG, fine-tuning or prompting? Cloud or private?
Two choices decide most designs. This is how we explain them in the first workshop.
RAG, fine-tuning or prompting?
| Approach | Use when | Strength | Limit |
|---|---|---|---|
| Prompting | The task is simple and needs no private data | Fast and cheap | Limited knowledge of your business |
| RAG | Answers must come from your documents | Current, cited and access-controlled | Depends on retrieval quality |
| Fine-tuning | You need a consistent style, format or domain behaviour | Shorter prompts and steady behaviour | Needs data and effort, and does not keep knowledge live |
| RAG plus fine-tuning | Complex enterprise assistants | Best quality | More to maintain |
Cloud model or private model?
| Factor | Cloud API | Private model on your servers |
|---|---|---|
| Data location | Sent to the provider under contract | Stays on your servers |
| Set-up | Fast | Needs GPUs and operations |
| Cost | Pay per use | Hardware and operations, often cheaper at steady high volume |
| Capability | The most capable frontier models | Strong open models, slightly behind on the hardest tasks |
| Control | The provider updates the model | You control versions and change |
| Best for | General use and lower sensitivity | Regulated or sensitive data |
Technology
Models and tools chosen for your data rules
We are vendor-neutral and test candidates on your own content before recommending one.
Models
Build and cloud
Data and serving
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Built for Saudi enterprises
Generative AI that respects your data and your rules
Local data, guardrails, governance and deployment choices are settled before the first prototype.
Decide where your data lives, then build
We classify the data an assistant will touch and choose hosting to match: on your servers, in a Saudi-hosted environment, or a cloud model with masking. Personal data is handled under the PDPL.
- Data classification before design
- On-premises or in-Kingdom options
- Masking before any external call
- No training on your data by default
- Retention and deletion rules
- Access reviews and logs
What you get
A defensible answer on data residency.
Rules the model cannot talk its way around
Guardrails sit around the model: they filter inputs, check outputs, enforce citations and block actions outside policy.
- Input and output filters
- Citation required for factual answers
- Refusal rules for out-of-scope questions
- Prompt-injection defences
- Personal data detection and masking
- Escalation to a person
What you get
Fewer risky answers and a record of every block.
Owners, records and review
Every assistant has an owner, a purpose, a risk rating and a review schedule, aligned with SDAIA's AI Ethics Principles and your own policies.
- An AI register with owners
- Risk assessment before launch
- Explainable answers with sources
- Model and prompt change control
- Incident handling
- Alignment with SDAIA principles, the PDPL and sector rules
What you get
AI that stands up to an audit.
Cloud, private cloud or on-premises
We deploy where your rules require and your team can support, and we can move between options as needs change.
- Hosted models with enterprise controls
- Private cloud deployment
- On-premises GPU servers
- Hybrid designs
- Monitoring and support
- Hand-over and training
What you get
A deployment your IT and security teams accept.
Clients
Banks and enterprises trust GoAgile with generative AI
We deliver financial AI reporting for Al Rajhi Bank, and RAG assistants and on-premises models for enterprises across Saudi Arabia. See financial AI reporting
Why GoAgile
Why enterprises build generative AI with GoAgile
Working demos are easy. These are the habits that make them reliable.
RAG configured for production
We have configured RAG pipelines for enterprises, with permission-aware retrieval and quality measured on real questions.
Private models on your servers
We have deployed models on-premises when data had to stay in-house, from GPU sizing to monitoring.
Guardrails and governance
Filters, citations, masking and audit trails are part of every build.
Connected to your systems
Assistants and agents work with your ERP, finance and document systems.
Arabic and English
Models and evaluation sets chosen and tested on your own bilingual content.
Local and accountable
A Riyadh team with ISO 27001 certified practices.
More from AI
Explore our other AI services
Each one stands alone and works better with the others: knowledge, agents, automation and the governance around them.
- AI Consulting & StrategyFind where AI pays off, set the guardrails and build a roadmap you can fund.View service
- Machine Learning & Data ScienceForecasting, anomaly detection and language models, trained and run where your data lives.View service
- AI AgentsAgents that take action in your ERP and service systems, with approvals, guardrails and audit.View service
- Intelligent Process AutomationDocuments, approvals and back-office work automated with RPA, AI and human review.View service
- AI Chatbots & Virtual AssistantsArabic and English assistants on web, WhatsApp and voice, handing off to people.View service
What would your teams ask if they could ask your documents?
Tell us the questions, the documents and the data rules. We will propose a pipeline, a model choice and a pilot plan.
Plan a generative AI project →Questions answered
Generative AI & RAG FAQs
What is RAG (retrieval-augmented generation)?
What is the difference between RAG and fine-tuning?
Can we run a large language model on our own servers?
Which LLM works best for Arabic?
How do you reduce hallucinations?
Is our data used to train the models?
What is a vector database?
How long does it take to build a RAG assistant?
Do you work with Microsoft Copilot and Azure OpenAI?
How much do GPUs for a private model cost?
Talk to a generative AI specialist
Tell us the use case, the documents and your data rules. We will respond with an approach and a pilot plan.
Why GoAgile for generative AI?
RAG, copilots and private models built with guardrails and local data control.
- RAG pipelines configured for enterprises
- Private LLMs on your own servers
- Guardrails and audit built in
- ISO 27001 information security practices