Task and model evaluation
Define the inputs, constraints and acceptance criteria with your team.
LLM application development
Build useful AI into everyday work.
Turn large language models into assistants for document review, customer support and internal operations.
For teams in the UAE, Saudi Arabia and across the GCC.
From requirement to implementation
What your project can include
Structured outputs and application APIs
Implement and test against the agreed workflow and representative data.
Quality, latency and cost monitoring
Make performance visible and prepare your team to operate the solution.
Designed around your environment
Start with the business requirement.
We scope the systems, data access, languages and deployment requirements before choosing models or infrastructure. Arabic and English quality should be tested on your actual terminology and user journeys.
Explore our Middle East approach →Bring to the discovery conversation
- The task you want to improve and how you measure it today.
- Your existing systems and a description of the available data.
- Required languages, users, hosting region and access restrictions.
- Your target timeline and an indicative budget range.
Final scope, delivery milestones and ongoing costs are agreed after discovery.
Before you start
A common question
Do we need to train a model from scratch?
Usually not. We start by evaluating existing models with your tasks, then assess prompting, retrieval or fine-tuning against that baseline.
Related services
ChatGPT / OpenAI integration
Integrate OpenAI APIs into your website, internal tools or software for assistance, extraction and task automation.
Explore service →Claude integration
Use Anthropic’s Claude API for document workflows, knowledge assistance and controlled tool-based tasks.
Explore service →LangChain development
Use LangChain components to implement model integrations, retrieval pipelines and tool-enabled assistants.
Explore service →Your next step
Tell us which task is slowing your team down.
Share the use case, your current systems and the outcome you want. We can use that conversation to define a practical first project.