Methodology
NLAI system stack
The four layers of a complete AI system: data infrastructure, AI systems, an intelligence layer and the operational outcomes they serve. We use it to check that a solution is designed as a whole, not as an isolated model.
Purpose
Why it exists
Many AI initiatives stall because one layer is built in isolation: a model without dependable data behind it, or predictions that never reach the people and processes that should act on them. The system stack is a simple check we apply to every design. Each layer has a clear job, and the interfaces between layers are defined explicitly.
The stack is technology-neutral. The same four layers apply whether a solution runs in a public cloud, in a cloud region in your country or on premises.
Method
How it works
04
Operational outcomes
The measurable results the system exists for, such as lower cost, higher throughput, better quality or reduced risk, tracked against the success definition agreed at the start.
- Cost
- Throughput
- Quality
- Risk
03
Intelligence layer
Decision support, prediction, anomaly detection and automation that turn model outputs into actions, with people in the loop where the stakes require it.
- Decision support
- Prediction and detection
- Automation with human review
02
AI systems
Model training, orchestration, serving and integration into operational workflows, including the evaluation needed to know when a model is good enough.
- Training and evaluation
- Serving
- Orchestration
01
Data infrastructure
Ingestion, cleaning, labelling, storage and streaming of the data the system depends on, with quality checks and lineage so that problems can be traced to their source.
- Ingestion and streaming
- Quality and lineage
- Storage and access
01
Assess
02
Advise
Stages where this framework is used
03
Build
Stages where this framework is used
04
Enable
05
Operate
06
Scale
Stages where this framework is used
In practice
Where it is applied
Offers that use it
Enterprise AI System
A production-grade AI system integrated into core operations, such as document intelligence, forecasting, computer vision or a knowledge assistant, engineered with monitoring, access control and governance from the start. Suited to organisations ready to move a proven use case into daily operations.
from SAR 120,000
Request a proposalAI Pilot Sprint
A time-boxed pilot that takes one prioritised use case from idea to a working solution on your own data, with success criteria agreed up front. It shows what the solution does in practice and what it would take to run it in production. Suited to organisations with a clear use case that want evidence before committing to a full build.
from SAR 75,000
Request a proposal
Billed in Saudi riyals. Starting prices are confirmed, together with the final scope and any applicable taxes, in your proposal or booking.
See all pricesRelated
Frameworks used alongside it
Enterprise AI Architecture
A reference architecture for running AI inside an enterprise estate: channels, orchestration and agents, models and retrieval, and data and integration, all within your security boundary, with identity, observability and governance across every layer.
MethodologyAdvise, Build and Scale
AI Governance Toolkit
A practical set of policies, templates and routines for using AI responsibly: who decides, how use cases are classified by risk, how systems are documented, how people stay in control and how incidents are handled. Proportionate by design, and built to work alongside applicable regulation.
GovernanceAdvise, Build and Operate
Engineering principles
Four principles that shape every system we design and build: outcome-driven, production-minded, responsible end to end, and engineered for your context.
MethodologyBuild, Operate and Scale
Next step
Apply this method to your programme
Tell us about your objectives, systems and constraints. We will show how this method shapes the work and what the first step would be.