Skip to content
/ Data Engineering /

AI Data Engineering Malta

AI data engineering in Malta. Build the data infrastructure that AI and machine learning models need: feature stores, training pipelines, ML data ops.

Book a free consultation → See how it works
/ the solution /

AI Data Engineering built around your business.

Every solution we deliver is built on three pillars: your data, your context, and continuous improvement. Each capability is traceable and measurable.

  • Feature Store Development

    Centralised feature stores that serve consistent, pre-computed features to both model trai…

  • Training Data Pipelines

    Automated pipelines that prepare, validate, version, and deliver training datasets for mac…

  • Data Labelling & Annotation

    Managed data labelling workflows combining human annotators with semi-automated labelling …

  • ML Data Quality Monitoring

    Continuous monitoring of data distributions, feature drift, label quality, and data freshn…

/ how it works /

Live in weeks, not months.

We work with your data science team to document feature requirements, data freshness needs, serving latency targets, and quality standards for each ML use case. This analysis shapes the data infrastructure architecture.

We design and build centralised feature stores with batch and real-time serving capabilities. Feature definitions, computation logic, and serving configurations are version-controlled and documented for team-wide reuse.

We build automated training data pipelines that produce versioned, validated datasets on schedule. Data augmentation, sampling strategies, and quality checks ensure training data meets model requirements consistently.

We configure labelling platforms, define annotation guidelines, implement quality controls, and establish review processes. Active learning integration prioritises the most informative samples for annotation to maximise labelling efficiency.

We deploy statistical monitoring that continuously compares production data distributions against training data baselines. Drift detection algorithms identify feature distribution changes, data quality degradation, and concept drift.

We integrate data infrastructure with your ML platform including experiment tracking, model registry, and deployment pipelines. End-to-end MLOps ensures seamless flow from data preparation through model training to production serving.

/ what you get /

Everything you need. Nothing you don't.

Feature Store
Development
Training Data
Pipelines
Data Labelling
& Annotation
ML Data
Quality Monitoring
/ built for your situation /

Sounds familiar?

Head of Data, retail group
"Our sales data lives in three different systems — Shopify, our ERP, and a warehouse management tool — and we can't get a single view of inventory performance"

We build a unified data pipeline that ingests from all three sources, applies consistent business logic, and loads into a data warehouse your BI team can query in real time.

How Neural AI helps

We build a unified data pipeline that ingests from all three sources, applies consistent business logic, and loads into a data warehouse your BI team can query in real time.

/ proof /

Real deployments. Real results.

Computer Vision

LiMap Site Deterioration Detection

We developed a custom computer vision model for AP Valletta that detects deterioration patterns including cracks, erosion, and staining from standard site photographs. The AI automatically maps detected damage onto AutoCAD drawings, reducing manual processing time by over 80%.

Automated training data pipeline processing 10,000+ annotated images

Read case study →
Data Engineering & ML

Tipico AML

We migrated Tipico's AML data science workflows from KNIME to Python-based big data analytics with AWS Airflow automation, achieving up to 70% faster ETL pipeline execution and improved risk-ranking accuracy.

Real-time feature serving for transaction risk scoring models

Read case study → Generative AI & RAG

Ligi.ai Legal Sector

Neural AI built Ligi.ai, a custom AI legal assistant for Maltese law firms that combines retrieval-augmented generation with deep knowledge of Maltese legislation. The system assists lawyers with document drafting, legal research across case law, and document review, reducing research time by over 70%.

Custom training data pipeline for legal document classification

Read case study →
/ questions /

AI Data Engineering FAQ

What is a feature store and why do we need one?
A feature store is a centralised repository for ML features that serves consistent data to both training and inference. Without one, teams often compute features differently for training versus production, causing training-serving skew that degrades model accuracy. Feature stores also enable feature sharing across teams, reducing duplicated engineering work.
How does AI data engineering differ from regular data engineering?
AI data engineering addresses ML-specific requirements that general data engineering does not cover: feature computation and serving, training data versioning and validation, data drift monitoring, labelling workflows, and the need for consistent data between training and inference environments. It is a specialised layer built on top of general data infrastructure.
What tools do you use for feature stores?
We work with Feast for open-source feature stores, Databricks Feature Store for Databricks-centric environments, AWS SageMaker Feature Store for AWS deployments, and custom feature store implementations when specific requirements warrant it. Tool selection depends on your ML platform, latency requirements, and existing infrastructure.
How do you handle data labelling quality?
We implement multi-annotator labelling with inter-annotator agreement measurement, review cycles for disagreements, and quality audits on random samples. Active learning identifies the most informative samples for annotation. Labelling guidelines are documented and iterated based on edge cases discovered during annotation.
What is data drift and how do you detect it?
Data drift occurs when the statistical distribution of production data changes from what the model was trained on, causing accuracy degradation. We monitor feature distributions using statistical tests like Kolmogorov-Smirnov, Population Stability Index, and Jensen-Shannon divergence, alerting your team when drift exceeds configured thresholds.
Can you work with our existing ML platform?
Yes, we integrate with popular ML platforms including Databricks MLflow, AWS SageMaker, Azure ML, Google Vertex AI, and open-source tools like Kubeflow and MLflow. Our data infrastructure feeds into your existing model training and serving pipelines without requiring platform changes.
How do you version training datasets?
We use DVC, Delta Lake versioning, or cloud-native dataset versioning to create immutable snapshots of training data. Each model training run references a specific dataset version, enabling full reproducibility. Version metadata includes data lineage, quality metrics, and the pipeline configuration that produced the dataset.
What about unstructured data like images and text?
Our pipelines handle unstructured data including image preprocessing, text tokenisation, embedding generation, and multimodal data alignment. We build specialised pipelines for computer vision training data, NLP corpora, and document processing datasets with appropriate augmentation and quality controls.
/ get started /

Ready to put AI to work in your business?

Book a free 30-minute consultation. We will map your highest-impact automation opportunities and give you a clear, no-obligation proposal.

Book a free consultation → Contact us →