LLM Integration Company UAE
Your Product, Powered by the Right Language Model.

We take a large language model — GPT, Claude, Gemini or an open-source model — and embed it into your existing product or internal systems so it does useful, reliable work. That means model selection, prompt architecture, retrieval of your data, tool calling, evaluation, and the engineering that makes it safe to ship to real users.

OpenAI (GPT-4o, GPT-4 Turbo), Anthropic Claude 3.5/4, Google Gemini, Meta Llama, Mistral, Cohere and self-hosted open-source models. We pick per task: a heavy model for reasoning, a smaller one for speed and cost, and an on-prem model where data cannot leave your network.

Yes, securely. We use retrieval-augmented generation (RAG) to ground answers in your documents and databases, with access controls, redaction and audit trails. For the highest-sensitivity cases we deploy an open-source model inside your own infrastructure so no data leaves your environment.

Model routing (cheap model first, expensive only when needed), caching, prompt optimisation, token budgets per request, and live cost dashboards. We design for a target cost-per-action and monitor it in production so a viral feature does not become a surprise invoice.

A single LLM feature (summarisation, classification, a copilot on one screen): 2-5 weeks. A grounded RAG system over your knowledge base: 5-10 weeks. A private, on-prem deployment with eval pipelines: 8-16 weeks. We always start with a proof of concept you can test.

A focused LLM feature starts around AED 25,000. A RAG knowledge system typically runs AED 60,000-200,000. A private on-prem LLM deployment is AED 200,000-600,000+, plus ongoing model and compute costs. You get a fixed, scoped quote after discovery.