Imagine running a winery but having to juggle satellite weather maps, soil data, grape genetics, supply chain numbers, and market trends — all in different formats, from different sources, and too much for any spreadsheet. This project built a set of software tools that pull all that messy data together, crunch it at scale, and give you predictions you can actually act on. Think of it as a smart dashboard for everything grape-related — from the vineyard to the bottle to the beauty cream. The same tools work for natural cosmetics companies that rely on grape-derived ingredients.
By the numbers
EUR 4,441,500
EU funding for development
10
consortium partners
6
countries represented
26
total deliverables produced
10
software demo deliverables
60%
industry partner ratio in consortium
4
SMEs in consortium
The business problem
What needed solving
Wine producers and natural cosmetics companies sit on mountains of data — satellite imagery, weather records, soil analysis, genetics, supply chain logs, market prices — but lack the tools to process it all together and extract actionable insights. Most of this data lives in silos, in incompatible formats, and at volumes too large for traditional software. Without integrated analytics, these companies make decisions based on gut feeling rather than evidence, missing opportunities and wasting resources.
The solution
What was built
The project built and cloud-deployed an integrated software stack with 10 major software components: data ingestion and integration tools, semantic data modelling and linking, distributed indexing, large-scale analytics and processing engines, predictive analytics for extremely large datasets, NLP-based linguistic pipelines for extracting knowledge from unstructured text, uncertainty-aware visual analytics dashboards, distributed inference tools, resource optimization algorithms, and documented APIs tying it all together.
Audience
Who needs this
Wine cooperatives and large wineries looking to integrate vineyard sensor, satellite, and sales dataNatural cosmetics companies needing better traceability of grape-derived ingredientsAgTech startups building precision viticulture platformsFood supply chain companies wanting predictive analytics across multiple data sourcesAgricultural data service providers seeking ready-made big data processing components
Business applications
Who can put this to work
Wine Production
mid-size
Target: Mid-size to large wineries and wine cooperatives
If you are a winery dealing with scattered data from satellite imagery, weather stations, soil sensors, and sales channels — this project developed an integrated software stack with predictive analytics that processes all of it in one place. The platform was built and deployed in the cloud with APIs, meaning your IT team can plug it into existing systems. With 10 demo deliverables covering everything from data ingestion to visual analytics, it addresses the full data pipeline a modern winery needs.
Natural Cosmetics
SME
Target: Cosmetics companies using grape-derived or plant-based ingredients
If you are a cosmetics manufacturer sourcing grape-based ingredients and struggling to track quality, origin, and supply chain data across suppliers — this project built data modelling and linking components that create a unified view of your ingredient data. The linguistic pipelines for semantic enrichment can automatically extract knowledge from unstructured supplier documents and research papers, saving hours of manual work.
Agricultural Technology
any
Target: AgTech companies providing data services to vineyards and farms
If you are an AgTech company building data products for precision viticulture — this project developed distributed indexing and processing components designed to handle extremely large datasets from satellites, weather systems, and field sensors. The resource optimization methods help reduce cloud computing costs when processing massive geospatial datasets. The entire stack was deployed in the cloud with documented APIs, ready for integration.
Frequently asked
Quick answers
What would it cost to adopt this technology?+
The project was funded with EUR 4,441,500 in EU contribution across 10 partners over 3 years. The integrated software stack was deployed in the cloud with open APIs. Licensing and pricing details are not specified in the project data — you would need to contact the coordinator AGROKNOW for commercial terms.
Can this handle industrial-scale data volumes?+
Yes, the project was specifically designed for extremely large datasets. Deliverables include distributed indexing components, resource optimization methods, and distributed inference tools — all built to process data at scale in cloud environments. The predictive analytics tools were tested on very large datasets according to the deliverable descriptions.
What about intellectual property and licensing?+
The project produced 26 deliverables including 10 software components. The coordinator is AGROKNOW, a Greek SME, with 6 industry partners in the consortium. IP arrangements would depend on the consortium agreement — contact the coordinator for licensing options.
Does this integrate with existing farm management or ERP systems?+
The project delivered an integrated software stack with documented APIs deployed in the cloud. The data ingestion and integration components were designed to pull data from multiple sources. This API-first architecture suggests straightforward integration with existing systems.
How mature is this technology — is it ready to use today?+
The project ran from 2018 to 2020 and produced working software deployed in the cloud. However, as a Research and Innovation Action, it was primarily focused on developing and demonstrating technology rather than commercial deployment. The tools have been tested but may need further productization for commercial use.
What types of data can this platform handle?+
Based on the project objective, the platform handles satellite and weather data, environmental and geological data, phenotypic and genetic plant data, food supply chain data, and economic and financial data. The data modelling components support semantic linking across these different data types.
Is there regulatory compliance built in?+
Based on available project data, the platform focuses on data processing, analytics, and interoperability rather than regulatory compliance specifically. The objective mentions helping companies achieve secure flow of their data, but specific compliance certifications are not detailed in the deliverables.
Consortium
Who built it
The BigDataGrapes consortium has a strong commercial orientation with 60% industry partners (6 out of 10) and 4 SMEs, spread across 6 European countries (Belgium, Bulgaria, Germany, Greece, France, Italy). The coordinator AGROKNOW is a Greek SME specializing in agricultural data — they are the most likely commercialization path for this technology. The mix includes 2 universities and 2 research organizations providing scientific depth, while the industry-heavy composition suggests the tools were built with real business needs in mind. The geographic spread covers key European wine-producing countries (France, Italy, Greece), which adds domain credibility.
AGROKNOW IKE is a Greek SME specializing in agricultural knowledge management — they coordinated this project and are the primary contact for licensing and collaboration.
Want to explore how BigDataGrapes technology can improve your wine or cosmetics data operations? SciTransfer can connect you directly with the development team and help evaluate fit for your business.
