Through a multi-year agreement with Qatar's Ministry of Communications and Information Technology (MCIT), Scale contributes alongside other partners to build that infrastructure. Across Scale's work with governments, AI is starting to take the same shape as other digital infrastructure: a shared layer designed for use across the public sector. Scale builds for that model, so components developed for one use case can carry over to the next as the architecture grows.
Where the Infrastructure is Being Applied
Scale, through its partnership with Qatar's Ministry of Communications and Information Technology (MCIT) is contributing to projects across several sectors of Qatar's national AI work. The examples below are a snapshot, not the full picture.
- Judicial: A central legislative body is using AI to accelerate legislative drafting and benchmarking, mapping draft clauses against relevant law, surfacing inconsistencies, and generating structured annotations in the formats legal teams already use. In a pilot, the workflow is accelerating processing time from 35 to 5 days, with human review remaining central at every step.
- Education: A personalized learning system is being built to serve a large number of students once fully deployed. AI tutors aligned to the national curriculum are designed to support concept mastery and practice, and teacher assistants are expected to reduce lesson planning time. The platform is now entering school pilots.
- Employment: Qatar's national recruitment system now includes AI-guided CV creation. In the first four weeks after launching, a quarter of new platform users voluntarily chose the AI tool, an adoption signal that matters because it wasn't mandated.
- Culture: The National Cultural Organization has launched an AI tour guide that personalizes how visitors discover art, heritage, and public spaces.
What connects these sectors is the foundation they are built upon. That foundation is a shared set of components that any new use case can inherit, which is why a tool built for one institution can power something different at another.
A Shared Architecture
Capabilities built for one use case become available to the others. When a new evaluation method, an improved oversight control, or a better data pipeline is developed, it becomes available across the portfolio. That is the mechanical difference between a set of projects and a piece of infrastructure: whether improvements compound across the system or stay trapped inside a single application. In practice, document understanding and legal retrieval capabilities built for one legislative workflow now also power a state cases assistant and a judges' preparation tool at separate institutions, without being rebuilt for each.
Data and processing controls stay inside Qatar, with sensitive workflows running on-premises and integrated with the government systems that already exist. This is what makes the system usable for workflows that would otherwise be off-limits to AI, and it is a precondition for MCIT treating AI as national infrastructure rather than as a vendor service.
The shared capabilities available across the system include:
- Oversight and red-teaming aligned to Qatar’s values and legal requirements
- Evaluation frameworks that test whether systems behave as intended
- Data operations that produce localized, region-specific training data
- An upskilling layer that trains the public servants who will actually use the systems
Humans at the Center
Workforce development is central to NDS3. Scale's upskilling and training programs are built to support that goal:
- Over 400 government ICT professionals have completed advanced AI training in technical maintenance and development.
- More than 1,600 across the government workforce have been trained on AI usage to support adoption across industries.
- More than 1,000 students have received foundational AI training to build long-term talent pipelines.
Training the public servants, students, and founders who actually work with these tools is what turns deployed software into working infrastructure.
The Underlying Pattern
Across sectors, Scale builds these systems so that AI scales expertise without displacing the humans who provide it. Legal experts make the legal judgments; the AI compresses the research and benchmarking around them.
This matters because the real question people have about government AI is whether it is going to replace the people they rely on. The pattern across these deployments points the other way: AI lets those people reach more people, faster, with better context. That is also what makes the infrastructure sustainable. Systems that try to remove human judgment get rejected by the institutions they're deployed into. Systems that augment it get adopted.
Amplified Expertise, Navigable Systems
When AI is built as a shared system, the benefits compound across it. Improvements made for one institution can reach others. Tools developed for one workflow can inform another. That is the difference between a portfolio of projects and a piece of infrastructure. Through its partnership with MCIT, Scale is contributing to that work in Qatar. Learn how Scale partners with governments building AI into national infrastructure here.
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