Methodology
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.
Purpose
Why it exists
Moving AI from experiments into daily operations raises the same questions every time. Where do models run, and where does the data stay? How do agents reach core systems safely? How is behaviour monitored, and who can see what?
The Enterprise AI Architecture answers these questions with a layered reference design and a short list of decisions to make explicitly for each organisation: model hosting (a managed service, a private deployment or on premises), data residency, integration patterns, identity and access, and the observability needed to operate the system.
It is vendor-neutral. We adapt it to your existing platforms and to your data residency requirements, including keeping data in-country where that is required.
What it needs
- 01Current architecture, platforms and integration landscape
- 02Security, identity and data residency requirements
- 03The use cases the architecture must support first
- 04Operational constraints: availability, latency and support model
Method
How it works
Your security boundary: cloud tenancy or on premises
01
Channels & experience
- Employee assistants
- Customer channels
- Business applications
02
Orchestration & agents
- Workflow orchestration
- Agents and tools
- Guardrails
03
Models & retrieval
- Language and ML modelsLLM · ML
- Retrieval over your knowledgeRAG
- Evaluation
04
Data & integration
- Enterprise systemsERP · CRM
- Data platform
- APIs and eventsAPI
Across every layer
- Identity and access
- Observability
- Governance and audit
Step by step
The steps in detail
01
Baseline the landscape
Map the current platforms, data flows, integration points and security zones that AI workloads will depend on.
02
Make the key decisions
Decide explicitly on model hosting, data residency, integration patterns, identity and the build-or-buy balance, and record each decision with its reasoning.
03
Tailor the reference design
Adapt the layered reference architecture to your estate and to the first use cases, including the non-functional requirements.
04
Define the path to production
Specify environments, deployment pipelines, evaluation gates and monitoring, so the first use case goes live on the same foundations later ones will use.
Outputs
What you receive
- 01
Current-state architecture map
- 02
Architecture decision records
Each key decision with the options considered and the reasoning.
- 03
Target reference architecture
- 04
Security and data residency design
- 05
Path-to-production specification
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
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.
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.