Across every practice
Operate & Scale
Keeping AI and digital systems reliable, useful and improving after go-live.
Launching a system is the start of its life, not the end of a project. Operate & Scale runs across all four practices: we monitor performance, data quality, cost and model behaviour, manage the model lifecycle, handle incidents and changes through an agreed support model, and extend what works to new teams and sites. Responsibilities and response commitments are defined per engagement, never as generic promises.
01Problems we solve
When this practice is the right place to start
The situations this practice is designed for. If several sound familiar, it is worth a conversation.
- 01
Systems went live, but no one is clearly responsible for monitoring quality, cost and incidents.
- 02
Model accuracy is drifting as data, products and behaviour change.
- 03
Your team lacks the MLOps capacity to retrain, release and roll back models safely.
- 04
Automations fail quietly when upstream systems change.
- 05
A successful pilot needs to reach more departments, sites or countries without starting again.
02Offerings
What we offer, and what each one delivers
Each offering is scoped separately, so you can start with the one that answers your most pressing question.
- 01
Managed AI operations
Outcome
Your AI systems monitored and maintained, with clear ownership of incidents and changes.
- 02
Monitoring and optimisation
Outcome
Performance, data quality, cost and model behaviour tracked and improved against agreed indicators.
- 03
Model lifecycle and MLOps
Outcome
Models retrained, evaluated, released and rolled back through a controlled pipeline.
- 04
Managed automation
Outcome
Automated workflows kept running as the systems around them change.
- 05
Support model
Outcome
Responsibilities, escalation paths and response commitments agreed in your contract.
- 06
Advisory retainer
Outcome
Ongoing senior advice on priorities, governance and the next initiatives to scale.
03Lifecycle position
Where Operate & Scale sits in the lifecycle
The highlighted stages are the ones this practice leads. The stages before and after connect to the other practices, so nothing is lost at a handover.
01
Assess
We establish where you stand: business priorities, processes, data, systems and team readiness. The result is a clear baseline and a shortlist of opportunities worth pursuing.
02
Advise
We turn the assessment into decisions: a prioritised roadmap, a target architecture, the assumptions behind the business case and the governance needed to act with confidence.
03
Build
We design and engineer the solution in short, reviewable increments, validated against real data and agreed acceptance criteria before anything reaches production.
04
Enable
We prepare your people to own what has been built: role-based training, documentation, a structured handover and the operating routines that make adoption last.
05
Operate
Led by this practice
We run and monitor the system alongside your team, covering performance, data quality, cost and model behaviour, with clear responsibilities for incidents and change requests.
06
Scale
Led by this practice
Once the value is proven, we extend what works to new processes, sites and business units, building on the same foundations instead of starting again.
04How an engagement runs
Phases and deliverables
Each phase ends with a review point and concrete deliverables, so you decide on the next phase with evidence in hand.
01
Transition
We take over documentation, access and monitoring for the system, agree the baseline indicators and confirm who does what.
Deliverables
- Operational baseline and indicators
- Responsibility matrix and escalation paths
02
Run and monitor
Performance, data quality, cost and model behaviour are monitored continuously, and incidents and change requests follow the agreed process.
Deliverables
- Monitoring dashboards and alerts
- Incident and change log
03
Review and improve
Regular reviews compare the system with its indicators and agree improvements, retraining or configuration changes.
Deliverables
- Periodic service review
- Improvement backlog
04
Release
Improvements, retrained models and updates pass evaluation and a controlled release with a rollback path.
Deliverables
- Evaluation results per release
- Release notes
05
Scale
What works is extended to new processes, sites or business units on the same foundations, with an updated business case.
Deliverables
- Roll-out plan
- Updated business case
05Works with
Operate & Scale runs across every practice
Systems, platforms and programmes delivered by any practice can move into operation under one support model.
- Advisory & Assessment
Decide where AI creates value before you invest in building it.
- Intelligent Systems & Automation
AI and automation engineered into the processes that run your organisation.
- Digital Platforms & Products
The data foundations and digital products that turn AI into everyday tools.
- Training & Capability Building
Leaders and teams who can use, govern and build with AI themselves.
06Technical depth
Capabilities in this practice
The architecture, methods and technologies behind each capability, written for technical teams.
Validation & Reliability
Model evaluation, robustness testing, real-world validation and post-deployment performance monitoring that keep AI dependable in operation.
MLOps & Model Lifecycle
The pipelines and routines that keep models current after launch: versioned data and models, automated retraining and evaluation, controlled release and rollback, and cost monitoring.
07Industry contexts
Where this practice applies
Sector challenges and the capability patterns that address them.
08Entry points
How to start, and what it costs
Positioning prices in SAR. Scope, assumptions and the final price are confirmed in a written proposal.
Managed AI Partnership
Ongoing operation and improvement of your AI and data systems after go-live: monitoring performance, data quality, cost and model behaviour, handling changes and planning the next improvements with your team. Suited to organisations that want their systems to keep delivering value without first building a full in-house operations team.
from SAR 15,000 per month
Request a proposal
09Questions
Frequently asked questions
Do you offer fixed response times?
Response commitments are agreed per contract, based on how critical the system is and the support hours you need. We do not publish generic service levels.
Can you operate systems that NLAI did not build?
Yes, after a transition phase in which we review the architecture, documentation, monitoring and access. Any gaps are reported to you before we take on operational responsibility.
What does model lifecycle management include?
Monitoring for drift, periodic evaluation, retraining when needed, controlled release with a rollback path, and a record of which model version is in use.
Can we start small?
Yes. Operate & Scale can start with monitoring and a monthly review for a single system, and expand as more systems go live.
Next step
Have systems in production that need a clear owner?
We will review what is running today and propose an operating model with defined responsibilities, indicators and review points.