Practice
Intelligent Systems & Automation
AI and automation engineered into the processes that run your organisation.
We design and build AI systems that do real work inside your operations: agents that complete multi-step tasks, knowledge systems that answer from your own documents in Arabic and English, document and vision pipelines, predictive models and automated workflows. Every system is evaluated against agreed criteria, integrated with the platforms you already run and handed over with the documentation needed to operate it.
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
Teams spend hours on repetitive document, data-entry and routing work that follows clear rules.
- 02
Knowledge is scattered across documents, systems and people, so the answer depends on who you ask.
- 03
A promising AI prototype exists, but no one has made it reliable, secure and integrated enough for daily use.
- 04
Decisions on demand, capacity, risk or maintenance still rely on spreadsheets and experience alone.
- 05
Arabic and English content must be handled with the same quality, not as an afterthought.
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
Enterprise AI agents
Outcome
Multi-step work completed across your systems, with human approval where it matters.
- 02
Knowledge and RAG systems
Outcome
Answers from your own documents with citations, respecting who is allowed to see what.
- 03
Document intelligence
Outcome
Arabic and English documents turned into validated, structured data.
- 04
Intelligent automation
Outcome
Rule-based, repetitive processes automated end to end, with a full audit trail.
- 05
Predictive and vision models
Outcome
Forecasts, anomaly alerts and visual inspection built into operational decisions.
- 06
AI Pilot Sprint
Outcome
A working pilot on real data, with an evidence-based decision on whether to scale.
03Lifecycle position
Where Intelligent Systems & Automation 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
Led by this practice
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
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
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
Discover
We start from the process, the data and the constraints, not from the technology. Interviews, sample data and a written definition of success set the target.
Deliverables
- Written success criteria
- Data sample and feasibility notes
02
Design
Architecture, data contracts, evaluation criteria, security and access design, and a milestone plan you can hold us to.
Deliverables
- Solution architecture and decision log
- Evaluation plan and milestone plan
03
Build and validate
Short increments with measurable checkpoints. Each increment is tested against evaluation sets built from your own data and reviewed against the success criteria.
Deliverables
- Working increments in your repositories
- Evaluation and validation reports
04
Deploy
Rollout into your environment with monitoring, access controls and a rollback path, integrated with the systems your teams already use.
Deliverables
- Deployment configuration and infrastructure as code
- Monitoring dashboards and runbooks
05
Hand over
Documentation, knowledge transfer and a clear support arrangement, so your team can own the system or continue with Operate & Scale.
Deliverables
- Technical and operating documentation
- Knowledge-transfer sessions
05Technical depth
Capabilities in this practice
The architecture, methods and technologies behind each capability, written for technical teams.
AI Systems Engineering
Computer vision, natural language, deep learning, multi-modal and predictive systems engineered for real-world deployment, not demonstrations.
Generative AI
Custom AI assistants, document intelligence, content generation and enterprise copilots, grounded in your own data.
Enterprise AI Agents
AI agents that plan and complete multi-step tasks across your systems: gathering information, preparing work and taking actions through approved tools.
Knowledge & RAG Systems
Enterprise knowledge systems that answer questions from your own policies, procedures and documents, with citations to the source.
Document Intelligence
Arabic and English documents — forms, invoices, contracts, certificates and correspondence — turned into validated, structured data.
Intelligent Automation
RPA, workflow automation, decision engines and intelligent document processing for operational efficiency.
AI Customer Experience
Smart chat and voice assistants, conversation analytics and agent-assist tools for customer service.
Industrial AI
Quality inspection, predictive maintenance, process anomaly detection and digital-twin foundations for production environments.
Robotics & AI Integration
Robot vision, motion intelligence, cobot integration and human-robot collaboration systems.
Edge AI & Real-time
On-device inference, low-latency systems, industrial camera integration and real-time processing pipelines for AI that runs where the data is created.
AI Financial Systems
Financial forecasting, fraud and risk scoring, automated accounting and decision support for finance functions.
AI Research & Development
Novel architectures, algorithm optimisation, evaluation-first prototyping and applied research for problems that off-the-shelf approaches do not solve.
06Frameworks
Methods we apply
Named NLAI frameworks that give the work in this practice a consistent structure.
Automation Discovery Framework
A structured way to find the processes where automation and AI agents will pay off, moving from a broad process inventory through three gates to a small backlog of candidates, each with a design and a business case.
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.
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.
NLAI system stack
The four layers of a complete AI system: data infrastructure, AI systems, an intelligence layer and the operational outcomes they serve.
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.
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 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
09Evidence
Related work
Projects and programmes connected to this practice, described with the role NLAI actually had.
- Collaborative research
Vision for Robotfusion
NLAI takes part in Vision for Robotfusion, a collaborative project in the Brabant AI community whose partners include Brabant.ai, Brainport Development and Breda Robotics.
Read more
10Questions
Frequently asked questions
Where do the systems run?
In the environment you choose: your own cloud tenancy, a cloud region of your choice or on-premises infrastructure. Data-residency requirements are agreed during design and reflected in the architecture.
Do the systems work with Arabic content?
Yes. Arabic and English retrieval, extraction and evaluation are designed in from the start, including mixed-language documents and right-to-left interfaces.
How do you know a system works before it goes live?
Success criteria are agreed in writing at the start, and every increment is tested against evaluation sets built from your own data. The results are shared with you before any release decision.
Who owns the code and the models?
Ownership is set out in the contract. Our standard approach delivers source code, configuration and documentation into your own repositories.
Next step
Have a process or prototype that should become a working system?
Tell us about the process, the data and the constraints. We will propose how to prove the value first, then how to build it properly.