Build Service Dubai, UAE
AI that thinks
with your team
Applied AI for businesses: systems built on large language models and your own data that make real work faster, safer and better, with people still in charge.
The useful question is not what AI can do.
It is which of your problems it can actually solve, and how you would know.
We build AI systems that sit inside real workflows: retrieval over your documents, assistants that know your products, classification and extraction that remove manual steps, and the evaluation harnesses that prove they work before anyone relies on them. Model choice, data handling and guardrails are engineering decisions here, made and documented.
For organisations with real work to speed up and real data to do it with.
Operations and customer teams drowning in documents and tickets, product companies adding intelligence to what they sell, and leadership teams who have been asked for an AI strategy and want something that ships.
- Skilled people spend hours a week finding, reading and copying
- Your knowledge is in documents nobody can search well
- A pilot impressed the board and never reached production
- You need to say exactly where customer data goes, and cannot
What an AI engagement covers
Opportunity mapping
Where AI would change an outcome in your business, ranked by value and feasibility, with the data each one needs and the risks each one carries.
Retrieval and knowledge systems
Search and question-answering over your documents, tickets and data, with sources cited and access controls respected.
Models, prompts and evaluation
Model selection, prompt and tool design, fine-tuning where it earns its cost and an evaluation set that measures quality on every change.
Deployment and governance
Production infrastructure, monitoring, cost controls, human review where it matters and a clear account of what data goes where.
How we work
Map
Two weeks on your workflows and data to find the problems worth solving and the ones to leave alone.
Prove
A working prototype against real data, measured with an evaluation set, before committing to a build.
Build
Production system with monitoring, guardrails and cost controls, integrated where the work happens.
Improve
Evaluation runs on every change, feedback loops from users and a model upgrade path.
What we believe about AI
Start from the problem
The question is never what the model can do. It is which outcome in your business would change, and how you would measure it.
Measure, do not demo
A demo works once. An evaluation set tells you how often it works, and whether the next change made it better or worse.
People stay in charge
AI proposes, people decide where it matters. Human review, cited sources and clear limits are design choices, made on purpose.
What you walk away with
- AI inside the workflow, not beside it
- Answers with sources, from your own data
- Quality measured, not assumed
- Clear governance over data, cost and risk
Questions we are asked about AI
Which models do you use?
Whichever fits the task, cost and data constraints: Anthropic, OpenAI and Google models through their APIs, and open-weight models hosted privately where data must not leave your environment.
Is our data used to train anything?
No. We use APIs and deployments with no training on your data, and we document exactly what is sent where. Fully private hosting is available when the sector requires it.
How do you stop the system making things up?
Retrieval with cited sources, constrained outputs, evaluation sets that test for it and human review on the decisions that matter. No system is perfect; ours tell you how confident they are.
How long before something is in production?
A proven prototype in four to six weeks; a production system in three to four months depending on integrations.
