ML & Data Engineering

Data architecturethat runs in production

No PowerPoints. We ship real architectures: Olearia Intelligence (70M+ data points, 166 extractors), Digital Twins (real-time IoT+ML). AIXA methodology validated in critical 24/7 systems.

See real cases Architecture audit (30 min)

Data Architectures

Data Lakes, Pipelines and Real-Time

We design modern, scalable architectures. We do not chase trends — we pick technology validated in production.

Modern Data Lakes

Structured architecture to store raw data (structured, semi-structured, unstructured) with clear governance. Guaranteed ACID transactions.

Real AIXA case

Olearia Intelligence: 70M+ olive oil observations from 9 official sources (European Commission, Eurostat, ECB, FRED, IOC, European satellite programs…)

Technology stack

Modern data architecture

ETL/ELT Pipelines

Extract, Transform, Load (ETL) vs Extract, Load, Transform (ELT). We pick by volume and complexity. Automated orchestration. Failure monitoring, automatic retries, alerting.

Real AIXA case

Olearia: 166 automated extractors running 24/7. If an extractor fails, exponential retry. If it persists, automatic alert.

Technology stack

Data orchestration

Real-Time vs Batch

Not everything needs to be real-time. Batch (daily/hourly) for historical data, real-time (<1s latency) for operational dashboards. Streaming when context demands it.

Real AIXA case

Olearia Digital Twins: real-time IoT. Olearia Intelligence: daily batch (historical prices).

Technology stack

Processing matched to the use case

Modern Data Warehouses

Architecture optimized for analytics. Compute/storage separation. Elastic scaling. Columnar compression. Standard SQL.

Real AIXA case

Aggregated multi-country reports, cohort analysis, forecasting on clean historical data.

Technology stack

OLTP and OLAP systems where each fits

MLOps

Machine Learning in Production (not Jupyter Notebooks)

Training models is easy. Keeping them running 24/7 without degrading is real engineering.

Training

Reproducible pipelines with experiment tracking. Versioned datasets and models. Feature stores for reuse.

Antipattern

Model trained locally with no version control.

AIXA approach

ML CI/CD: automated training, metric validation, versioned registry, automated deploy when baseline is beaten.

Deployment

Containers exposing REST APIs to serve models. Orchestration for scaling and health checks. A/B testing to validate the new model against baseline.

Antipattern

Model in a script with no control or monitoring.

AIXA approach

REST API + containers + orchestration. Health and metrics endpoints. Automated rollback on degradation.

Monitoring

Models degrade over time (concept drift, data drift). Continuous monitoring: feature distribution, predictions, business metrics. Automatic retraining when drift is detected.

Antipattern

Model deployed and never retrained.

AIXA approach

Continuous monitoring. Automated alerts on degradation. Retraining cadence based on data volatility.

Governance

GDPR, explainability, model auditing. Decision logging: which model, which version, which features, which prediction, when.

Antipattern

Model in production with no traceability.

AIXA approach

Full model and decision logging. Structured logs. Guaranteed auditability.

Methodology

Methodology : From Audit to Production

A four-phase structured process. We do not start building until we understand your context.

Assessment

We understand your current architecture. We audit where data lives, its quality and how it flows. We identify priority improvements and critical technical debt.

Clear map of your current architecture

Identification of priority improvements

Realistic action plan

Intelligence

We design the ideal architecture for your case. We pick technology by real need, not trends. We validate the design with you before building.

Understandable technical design

Justified technology stack

Validated implementation plan

eXecution

We build the solution step by step. Incremental deployment showing constant progress. Your team learns to use it from day one.

System running in production

Trained team

Clear documentation for autonomous operation

Amplification

We optimize and scale. We monitor real usage, adjust based on data and plan continuous improvements.

Regular reports

Improvements grounded in real usage

Ongoing support

See full methodology

Stack

Technology stack validated in production

We do not follow Hacker News. We use technology proven in 24/7 critical systems.

Backend & Data

High-performance REST APIs

Relational databases

In-memory cache

Asynchronous processing

Object storage

Machine Learning

Deep learning frameworks

Classical machine learning

Time series analysis

Model interoperability

Experiment tracking

Frontend & Viz

Modern web applications

Interactive visualizations

Responsive design

Reusable components

Infrastructure & Cloud

Orchestrated containers

Automated CI/CD

Monitoring and alerting

Reverse proxy

SSL certificates

Cloud Strategy

Multi-cloud strategy

European providers

Public cloud where it fits

CDN and DDoS protection

Real Cases

Real architectures running 24/7

We do not sell theory. These are systems running on real data, with real users, under real load.

Olearia Intelligence: 70M+ data points in production

Olive Oil Sector

Challenge

Integrate 9 official sources (European Commission, Eurostat, ECB, FRED, IOC, European satellite programs…) into a unified platform. 25 years of history, 25 Mediterranean countries. EVOO price prediction with ML.

Solution

High-performance REST API with asynchronous processing. 166 automated extractors running 24/7. ETL pipelines with exponential retry. Predictive models with automated retraining.

Outcome

70M+ verifiable olive oil observations. API latency <50ms (p95). 99.5% uptime. Weekly automated model retraining.

See full case

Olearia Digital Twins: olive mill digital twin

Industry 4.0

Challenge

Navigable 3D digital twin with real-time IoT, ML predictive maintenance, multi-line OEE. 18 operational modules. <100ms latency.

Solution

Advanced 3D navigation with high-performance rendering. Industrial IoT protocols for sensors. Database optimized for time series. ML predictive models. Installable web application.

Outcome

Working demo at olearia.io/digital-twins. 9 navigable 3D zones. Predictive maintenance. Architecture validated for Q1 2026 commercial launch.

See full case

FAQ

Frequently asked questions

We answer the most common questions about data engineering and ML projects.

What does a modern data architecture include?

It includes data lake design, data pipelines, source integration, production-grade predictive models and operational dashboards. The scope adapts to each organization's specific needs.

Do I need to migrate all my infrastructure at once?

No. Incremental approach: audit of current architecture, implementation of priority quick wins and phased migration. Legacy and modern systems can coexist during the transition.

What is the difference between you and large consultancies?

We ship code in production, not slide decks. Olearia Intelligence and Digital Twins are real products running with verifiable data. We show real architecture, not theory.

Do you only work in olive oil or other sectors too?

Olearia is specific to olive oil. We also do consultancy and development for other sectors: agri-food, manufacturing, tech startups. We assess feasibility for each project.

Do you only use open source or also managed cloud services?

Pragmatic, not dogmatic. We design the optimal architecture for each case. If your company is already on a cloud, we integrate there. We do not sell a fixed stack.

Do you train internal technical teams?

Yes. We include knowledge transfer: full architecture documentation, operations runbooks, pair programming with the internal team and targeted training on the chosen technologies. Goal: technical autonomy after the project.

What if the architecture does not scale?

Technical guarantee: if the designed architecture does not handle the specified load, we redesign at no extra cost. Example: Olearia Intelligence is designed with significant headroom over current requirements.

Have data and unsure how to structure it?

Data architecture audit (30 min)

More AIXA Capabilities

Explore more capabilities

Data engineering is the foundation of every intelligent system.

Olive Oil Sector

Olearia Intelligence ingests 70M+ data points with 166 24/7 extractors. See the real data architecture behind the ecosystem.

See Olearia Intelligence case

Industrial Digital Twins

Digital twins need ML in production. See how we embed predictive models into operational digital twins.

See digital twins

Years of data in silos and unsure how to unify them?

We audit your data architecture: identifying quick wins, critical technical debt and a prioritized roadmap. First consultation free (30 min). No commitment.

Free assessment (30 min) See Olearia case (70M+ data points)

Real cases: olive oil, agri-food, manufacturing. AIXA AI, Granada, Andalusia, Spain.