Capability
AI Systems Engineering
Computer vision, natural language, deep learning, multi-modal and predictive systems engineered for real-world deployment, not demonstrations. We treat data, models, inference and monitoring as one system, so what works in evaluation keeps working in operation.
We design end-to-end AI systems in which every component — data, model, inference and monitoring — is engineered to operate as one production-grade whole.
01Architecture
Reference architecture
A typical arrangement of the components. Every implementation is adapted to your systems, data-residency and security requirements.
Data sources
- Cameras and sensorsGigE · MQTT
- Documents and text
- Operational systemsERP · MES
Model development
- Feature and label pipelinesPython
- Training and evaluationPyTorch · MLflow
Inference
- Model servingTriton · ONNX Runtime
- Application APIsFastAPI
Operations
- Monitoring and driftMLflow
- Planning and quality tools
02Components
What this capability covers
01
Computer vision
Object detection, segmentation, OCR and visual quality systems for factory, warehouse and field environments.
02
Natural language
Information extraction, classification, semantic search and multilingual NLP pipelines for enterprise data, including Arabic.
03
Predictive modelling
Forecasting, anomaly detection and decision systems trained on your operational data and validated against ground truth.
04
Multi-modal systems
Pipelines that combine vision, language and sensor signals for richer downstream reasoning.
03Implementation
How we implement it
We start from an evaluation set built from your own data and a written definition of acceptable performance, and only then choose model families. Pipelines are versioned end to end, so every prediction can be traced to the data and model that produced it.
- Baselines first: simple models set the bar before complex ones are justified.
- Inference is packaged as a service with health checks, logging and a rollback path.
- Monitoring for data drift and prediction quality ships with the first release, not in a later phase.
04Deliverables
What you receive
Evaluation set and acceptance criteria
Trained models with model cards
Inference service and integration
Monitoring and retraining plan
Technology
Technologies we work with
- PyTorch
- TensorFlow
- ONNX Runtime
- OpenCV
- FastAPI
- Triton
- MLflow
- Docker
Chosen per project and aligned with your existing standards. Listed as technologies we use, not as partnerships or endorsements.
05Use cases
Typical applications
Patterns this capability is designed for, not a list of delivered projects.
01
Visual quality inspection
Defect detection on production lines with configurable tolerance bands, sized to the line's speed and inspection criteria.
Performance depends on your data, hardware and context, and is confirmed during the engagement.
02
Document intelligence
Structured fields extracted from contracts, invoices and certificates, validated before the data reaches downstream systems.
03
Operational forecasting
Demand, throughput and downtime prediction wired into existing planning tools.
06Evidence
Related work
Projects and programmes in which this capability played a part, 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
07Related
Where to go next
Industry contexts
More in Intelligent Systems & Automation
- 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.
Next step
Scope AI Systems Engineering for your organisation
Share your process, data and constraints. We will propose an approach, the evidence to collect first and a realistic plan.