Ship AI features that actually work in production — copilots, agents, RAG pipelines, evaluation frameworks, and LLM integrations — built by an experienced AI engineering team based in Dubai.
Businesses across the UAE are moving from AI experiments to AI products in 2026. The gap between a working demo and a production feature that handles real users, real data, and real edge cases is where most projects get stuck. Closing that gap is what our AI engineering team does.
We build AI features that ship — copilots that assist your team in their actual workflows, RAG systems that retrieve the right information reliably, agents that complete multi-step tasks autonomously, and evaluation frameworks that tell you whether your AI is performing well or silently failing.
RAG Systems and AI-Powered Knowledge Bases Retrieval-Augmented Generation is the most widely deployed AI pattern in business applications — and the one with the most ways to fail quietly. We build complete RAG pipelines from ingestion through production monitoring, with evaluation frameworks that measure whether retrieval and generation are actually working for your use case. Whether you're building an internal knowledge base, a customer-facing search product, or a document Q&A system, we've solved the hard problems: chunking strategy, embedding model selection, hybrid search, reranking, and hallucination reduction.
AI Copilots and Workflow Assistants A copilot that genuinely helps your team work faster is a specific engineering challenge — not a ChatGPT wrapper. We design copilots around real workflows, with proper context management, tool access, memory, and guardrails. The result is an AI assistant that understands your business context, has access to the right data, and produces output your team can trust.
LLM Agents and Tool Use Agents that can take actions — searching databases, calling APIs, writing files, sending notifications, triggering workflows — require careful architecture around tool design, error handling, and safety guardrails. We build agents that complete multi-step tasks reliably, with proper observability so you can see exactly what they did and why.
Model Evaluation, Guardrails, and Monitoring Production AI without evaluation is a liability. We implement evaluation frameworks tailored to your specific use case, input and output guardrails that prevent prompt injection and inappropriate outputs, cost and latency monitoring, and drift detection that alerts you when model performance degrades. These aren't optional extras — they're the difference between AI that you can rely on and AI that surprises you in production.
LLM Application Backends and API Integrations We build the backend infrastructure that connects AI capabilities to your product — prompt management systems, streaming endpoints, rate limiting, caching layers, async processing pipelines, and integrations with your existing data sources and business systems.
Dedicated AI Engineer A senior AI engineer joins your team for a defined period — minimum three months — with full context continuity on your product and roadmap. Best for teams actively building AI products who need consistent, expert engineering resource without the overhead of a full-time hire.
Dedicated Squad An AI engineer paired with backend and frontend engineers from our team, working together on a unified delivery. Best for greenfield AI product builds or major feature additions that require coordinated full-stack work.
Project-Based Delivery A scoped engagement with a defined deliverable, timeline, and milestone structure. Best for specific feature builds — a RAG pipeline, an agent implementation, an evaluation framework — where the scope is clear and the outcome is measurable.
We are based in Dubai and we build for the UAE market — which means we understand the Arabic language requirements, the regulatory environment, the communication patterns of UAE businesses, and the specific infrastructure and compliance considerations that matter when you're deploying AI features for users in this region.
We don't pitch AI features that sound impressive but don't solve real problems. Every engagement starts with an honest assessment of whether AI is the right solution for the specific workflow, what it will cost to run at your usage volume, and what the realistic quality ceiling is given your data and constraints.
The projects we're most proud of are not the most technically ambitious ones. They're the ones where the AI feature became a genuine part of how our client's team works — used every day, trusted, and improving over time.