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Top 5 Agentic AI Development Companies for Enterprises in Dubai, Abu Dhabi, and the UAE (2026)
If you're an enterprise in the Middle East looking to deploy AI agents, you have more options than ever. Here is an honest look at the firms actually deploying production-grade systems.
If you're an enterprise in the Middle East looking to deploy AI agents, you have more options than ever. The UAE has become a legitimate AI hub, with both government-backed initiatives and private firms building production-grade agentic systems.
Here's an honest look at the five companies actually deploying AI agents for enterprises in Dubai and Abu Dhabi, what makes each one different, and which one might be right for your business.
What Enterprises Should Evaluate Beyond Brand Names
When choosing an AI partner, decision makers need to assess:
Model independence
Can the partner mix and match models based on task? Or are you locked into their proprietary stack?
Data governance
Will your data train models that leave your project? What are retention, deletion, and audit policies?
Operational ownership
Who handles reliability and bugs? How fast can you reach the engineering team during incidents?
Local hosting options
Can you keep data and inference on-premise or within your chosen cloud region?
Engineering partnership
Does the provider co-develop with your team or deliver a black box?
These criteria often drive enterprises with compliance, IP, or geopolitical concerns toward private, founder-led firms for specific projects.
1. G42
Government-Backed Scale
G42 is the 800-pound gorilla of UAE AI. Backed by substantial government investment and partnerships with global players, they're building large-scale AI infrastructure across the region.
What they're good at
If you need massive compute resources or you're a government entity looking for region-wide AI deployment, G42 has the scale and political backing to deliver.
Where they might not fit
Multinational corporations with strict data sovereignty requirements or companies operating across competing geopolitical regions may find government backing creates complications. G42's scale also means you're unlikely to get founder-level attention on your specific deployment.
2. Presight.ai
Big Data and Vision AI
Presight focuses heavily on computer vision and big data analytics. They've deployed surveillance and smart city infrastructure across the UAE.
What they're good at
If your use case involves video analytics, smart city infrastructure, or large-scale data processing, Presight has proven deployment experience.
Where they might not fit
Custom workflow automation that doesn't involve vision AI or big data. If you need agents that handle Sales, Operations, Finance, or HR workflows, you're looking at a different type of problem than Presight typically solves.
3. Core42
Cloud Infrastructure for AI
Core42 builds the infrastructure layer that other AI companies run on. Think cloud compute, data centers, and sovereign cloud solutions.
What they're good at
Infrastructure. If you're building your own AI systems and need UAE-based compute that meets data residency requirements, Core42 provides the foundation.
Where they might not fit
They're an infrastructure provider, not an AI agent developer. If you need someone to actually build and deploy agents for your specific workflows, you'll still need a development partner on top of Core42's infrastructure.
4. AI71
Open Source AI Research
AI71 is focused on open-source AI models, particularly their Falcon series. They're contributing to the broader AI research community and making models available for others to build on.
What they're good at
If you want to run open-source models locally and have the engineering team to build on top of them, AI71 provides solid foundation models with UAE-specific training data.
Where they might not fit
Most enterprises don't have the ML engineering capacity to take a foundation model and turn it into production agents. You get the model, but you still need to build the agents yourself.
5. Rifoa
Private, Founder-Led Development
Here's where we're different.
Rifoa is not government-backed. We're not building foundation models. We're not an infrastructure provider. We're a founder-led firm that deploys custom AI agents for enterprise workflows, and we do it alongside you from audit to production.
Why Work With a Private Company Instead of Government-Backed Players?
Data stays private
When you work with a government-backed AI company, there's always a question about where your operational data goes. Are you training their next model? Will your proprietary workflows end up in a national AI initiative? With Rifoa, your data is yours. We build agents using commercial APIs (OpenAI for coding tasks, Anthropic for reasoning tasks) that have enterprise data protection built in. No training on your data. No government reporting requirements.
No geopolitical complications
If you're a multinational operating in Dubai but headquartered in Europe or the US, using a UAE government-backed AI provider can create compliance headaches. Data sovereignty rules, export controls, and corporate governance all get messier when government backing is involved. Private companies don't carry that risk.
No risk of nationalization or competition
Government-backed companies can pivot. Priorities change. A company building AI agents for your industry today might decide to launch a competing service tomorrow, backed by the same government funding. With a private firm, that dynamic doesn't exist. We're not going to nationalize your deployment or turn your workflow automation into a government service.
Why Model-Agnostic Matters
We don't have a house model we're trying to push. We use whatever works best for your specific task:
Coding tasks
OpenAI's GPT-4o or Codex for code generation and debugging
Complex reasoning
Anthropic's Claude Opus for multi-step decision making
Cost-sensitive tasks
Claude Haiku for high-volume, lower-complexity workflows
Local deployment
Open-source models if you have data residency requirements
Real Example
We recently deployed a finance automation agent for a Dubai-based corporate. Invoice processing needed speed and cost efficiency (Claude Haiku). Contract analysis needed deep reasoning (Claude Opus). Code generation for integrating with their legacy ERP needed coding expertise (GPT-4o). Three different tasks, three different models, all deployed in the same workflow. A single-model shop would have forced everything through one model, sacrificing either cost, speed, or quality.
Intelligence Isn't Where You Should Cut Costs
The entire point of AI agents is to reduce human involvement in repetitive work. The more intelligent the agent, the less human oversight it needs. The less human oversight, the more it scales.
This is why we use frontier models from OpenAI and Anthropic instead of cheaper alternatives. Yes, local models are cheaper per token. But if the agent hallucinates or needs constant human correction, you've defeated the purpose. You wanted automation that runs independently. A cheaper model that needs babysitting isn't automation.
Concrete example
We deployed a tender tracking agent for a government contractor. The client wanted to use an open-source model to save on API costs. We ran a side-by-side test.
Open-source model
Missed 30% of relevant RFPs, required significant human editing. Saved AED 800/month but cost AED 200K in missed opportunities.
Claude Opus
Caught 95% of opportunities, produced drafts needing minimal editing. Now the client's standard.
Founder-Led Development Alongside You
Every other company on this list is big enough that you'll be working with an account manager, a solutions architect, and a development team. That's fine for large-scale infrastructure projects. However, for custom workflow automation, you work directly with the founder.
I visit your office in Dubai or Abu Dhabi. I watch your employees do their actual work. I see the Excel sheets they update manually. The invoices they process by hand. The emails they send one by one. Then we build agents that handle those exact steps.
Why this matters
Most AI consultants ask you to document your workflows, then build based on your documentation. But documentation always misses context. The workarounds employees use. The exceptions nobody wrote down. The part where someone downloads a PDF, copies data into a spreadsheet, then uploads it to another system because the two systems don't talk to each other.
I see that stuff because I'm there. Then we don't just automate the broken process. We redesign it to be AI-native.
Example: Hotel Group Workflow
A hotel group asked us to automate their guest communication workflow. They described it as "send booking confirmations and check-in reminders." When I visited their operations team, I saw they were manually copying booking data from their PMS into a spreadsheet, then using mail merge to send emails because their PMS email system couldn't handle custom templates.
We didn't automate the spreadsheet process. We rebuilt the workflow entirely. The AI agent now pulls booking data directly from the PMS API, generates personalized emails based on guest history (repeat visitor, first-timer, VIP status), and sends them automatically. No spreadsheet. No manual copying. The process that took 3 hours daily now takes zero.
That only happened because I was there to see the actual workflow, not just the documented one.
Always Available, Never Ghosted
Here's something nobody talks about: what happens when your AI agent breaks?
If you're working with a large consultancy or government-backed firm, good luck getting someone on the phone at 11pm when your production agent starts hallucinating. You'll file a support ticket. Wait for business hours. Get routed through tier-1 support who doesn't understand agentic systems.
I built your agent. I know exactly how it works. When it breaks, you call me. I fix it.
Process Redesign, Not Just Automation
Most companies automate their existing processes. We redesign processes to be AI-native.
Example: Java to Rust Migration
A client wanted to automate code review for a legacy Java application. The problem? AI models are trained primarily on Python, JavaScript, and Rust. Java is underrepresented in training data, so the AI struggled with their codebase.
The obvious answer: "Sorry, AI can't handle your Java project well enough yet."
The actual answer: Port the Java application to Rust. Use Claude Code to handle the port, then write end-to-end tests to verify correctness. Now you have a Rust codebase where AI code review actually works, plus you get Rust's performance and safety benefits.
With Claude Opus 4.5 and GPT-4o, automated code ports are about 95% accurate. The remaining 5% you catch with tests. The upside is years of AI-assisted development instead of waiting for models to eventually learn your legacy stack.
First-Mover Advantage in AI
Every month you spend watching AI develop without deploying it is a month your competitors spend building intuition.
The companies deploying AI agents now are making mistakes. They're learning what works and what doesn't. They're building processes around AI strengths and working around AI weaknesses. When models get more reliable in 6-12 months, these companies will already have operational muscle memory.
The gap between early adopters and late movers compounds exponentially, not linearly.
Which Company Is Right for You?
Choose G42 if you're a government entity or need massive regional compute resources and political backing matters more than data privacy.
Choose Presight if your use case is computer vision, smart cities, or surveillance infrastructure.
Choose Core42 if you're building your own AI systems and need UAE sovereign cloud infrastructure.
Choose AI71 if you have a strong ML engineering team and want to build on top of open-source models.
Choose Rifoa if you are an enterprise that needs custom AI agents deployed for Sales, Operations, Finance, or HR workflows, and you want founder-level attention from audit through deployment.
What an Engagement Actually Looks Like
1-2
Week 1-2: Discovery workshop and operations audit
We map your workflows, data flows, and regulatory constraints.
3-4
Week 3-4: Deploy a minimal viable agent
For one high-impact workflow. Prove it works in production.
2-3
Month 2-3: Iterate based on real usage
Add observability, expand scope, validate reliability.
3+
Month 3+: Handover
With documented automation blueprints, team training, and optional long-term support SLA.
No six-month consulting engagements. No strategy decks that sit on shelves. Working agents in production within 30-60 days.