Methodology
Delivery method
How the Build stage runs: five steps from a written success definition to a system that is deployed, observed and improving, with six deliverables that come with every build engagement by default.
Purpose
Why it exists
The engagement lifecycle shows where an engagement stands. The delivery method describes how the build work inside it is done, so that every project follows the same discipline whatever technology is involved.
It is designed to remove the two most common reasons technical projects disappoint: success that was never defined in writing, and systems that reach production without the monitoring, documentation and ownership needed to keep them working.
It is not a second lifecycle. It sits inside the Build stage, and it hands over to Enable and Operate.
What it needs
- 01An agreed use case with a named business owner
- 02Access to representative data and the systems to integrate with
- 03The constraints that apply: security, data residency, budget and timeline
- 04People who can review increments and accept or reject them
Method
How it works
01
Assess
02
Advise
03
Build
04
Enable
05
Operate
06
Scale
These steps run inside the Build stage of the engagement lifecycle.
01
Discover
02
Design
03
Build & validate
04
Deploy
05
Operate & improve
Step by step
The steps in detail
01
Discover
We start from the problem, the data and the constraints, not from a technology. Through workshops, interviews and data sampling we write down what success means and how it will be measured.
What happens
- Stakeholder interviews and process walk-throughs
- Data sampling and quality checks
Outputs
- Written success definition
- Constraints and risk register
02
Design
We define the architecture, the data contracts, the evaluation criteria and a milestone plan you can hold us to. Significant choices are recorded with their reasoning.
Outputs
- Architecture and decision log
- Milestone plan
03
Build & validate
We engineer in short increments with review points. Each increment is measured against the success definition with an evaluation harness, so progress is visible and problems surface early.
Outputs
- Working increments
- Evaluation results
04
Deploy
We move the system into production in a controlled way: a staged roll-out, a rollback plan, access controls and monitoring switched on before users depend on it.
Outputs
- Production release
- Runbooks
05
Operate & improve
After go-live we track behaviour, data quality and cost against the success definition, and agree an explicit rhythm for support, retraining and improvement.
Outputs
- Monitoring in place
- Improvement backlog
Outputs
What you receive
- 01
Solution architecture and decision log
How the system is built and why, with the options that were considered.
- 02
Source code in your repositories
Your organisation owns the code from the first commit.
- 03
Evaluation results and validation reports
Evidence that the system meets the agreed acceptance criteria.
- 04
Deployment configuration and infrastructure definitions
Everything needed to rebuild and redeploy the system.
- 05
Runbooks and monitoring
How to operate the system day to day and what to watch.
- 06
Knowledge transfer and team enablement
Your team can run, change and extend what was built.
In practice
Where it is applied
Offers that use it
AI 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 proposalDigital Platform / Product Build
Design and engineering of a digital platform or product, such as a customer portal, an internal operations system or a data platform, delivered in reviewable increments with security, performance and maintainability built in. Suited to organisations that need a dependable system built around their processes and integrated with the tools they already use.
from SAR 95,000
Request a proposalEnterprise 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 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
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
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
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
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.