Company Name:


Incubation Round:


Application Track:

Track 1

Proposed by:


Data Provider:


Challenge Name:

Optimisation of Maintenance Process

Product Description

AIGAD (Artificial Intelligence for Graph Neural Network-based Anomaly Detection) is a set of ontological GNN-based models that leverages the correlations between different sensor measurements in IoT Industrial Networks in order to spot potential operation anomalies in advance and act accordingly. Put differently, AIGAD’s main purpose is to capture complex inter-sensor relationships and detect and explain anomalies that deviate from usual functions by using AI-based technologies that can exploit underlying, non-visible properties of sensors that typically correlate when these unexpected behaviours show up. This will allow industrial processes to anticipate potential operation downturns and minimize their negative effects.

Usage of Standards for data interoperability:

With AIGAD our goals include but are not limited to, fostering interoperability by creating a WoT (Web of Things) ontological resolution middleware that will be used to map IoT sensor data to AIGAD GNN anomaly-detection resolution models. Our clients add their devices to the database of IoT devices available in the platform, also including a TD (Things Descriptor) instance describing the device they included. This action will allow the models to clearly establish in advance the type of devices, how data is formatted, etc. thus fostering scalability in AIGAD.

Reach Timeline

*Expose phase is open to all Experiment phase teams


Murcia, Spain


Energy | Manufacturing / Industry | Smart Cities | Transportation



Company maturity:

Live funding and product-market fit

Investment level:


Funding raised:

< EUR 500,000

Collaboration opportunity:

Company Description

Before developing AirTrace, we developed several projects, with special focus on industrial traceability, including Blockchain and Artificial Intelligence technologies. One of our previous projects is Block & Wine, developed jointly with Bodegas Emilio Moro where an integral traceability application was built upon Hyperledger Fabric and Alastria to monitor the logistic parameters in end-to-end wine production supply chains ( Apart from the Blockchain technology supported in this project, we also developed and integrated several algorithms for computer vision tasks (license plate identification, weight control, among others) which helped collect data throughout the value chain.

Apart from other projects, we also developed a traceability solution for Air-Quality continuous monitoring in a big manufacturing industry for the real-time collection of air-quality levels in an industrial scenario and its securitization in a permissioned Blockchain (Hyperledger Fabric). Apart from this, we have developed other projects like IAMAI, a platform for automatic data categorization and extraction in administrative documents such as invoices and bills.

Involvement in Standardisation Bodies:

Web of Things Community Group

Juan Bautista Tomás Gabarrón, CEO

PhD in Computer Science, 7 years as entrepreneur, expert in negotiation, AI and Blockchain lover.

Jesús Caicedo García, COO

ESADE Marketing degree, MBA James Cook University, expert in public relations and fundraising.

Luis Salas de Teodoro, Sales Director

Tech Entrepeneur, long experience in startup management. Blockchain in business expert.

Juan Miguel Navarro, AIGAD Technical Lead

PhD in Telecommunications, IoT Expert, several Research publications in IoT and AI in Academia.

Mariano Mateos Marín, CFO

B.Sc. and Master in financial services and entrepreneurship management, with extensive background in accounting and strategy.

Julio Marqués Eman, Marketing

B.Sc. in Computer Science, expert in SEO and technical copywriting, making airtrace search engine results stand out in just 2 months.