€200k · Other round · Foodtech · Valencia, Spain
Valencia-based ART Technologies, which builds hyperspectral inspection systems for food producers, has raised €200k in a funding round with investment from REDIT Ventures and its Investment Club. REDIT says the capital will support industrial and commercial development, manufacturing and deployment of the technology at scale.
The new €200k round follows €315k previously provided by AINIA and REDIT Ventures, which REDIT describes as strategic partners from the project’s early stages.
Moving hyperspectral inspection onto food production lines
ART Technologies is an AINIA spin-off built to industrialise research in hyperspectral vision. Its system analyses more wavelength information than a conventional colour camera, allowing food processors to examine internal and surface characteristics of bulk products in real time. The company and REDIT say the technology can identify foreign bodies that are not visible to the human eye, with the aim of making quality checks more precise and automated. The company’s product page positions the system around deep, precise inspection of bulk food.
The commercial challenge is therefore not only whether the sensing works in a controlled setting. ART Technologies must turn technology developed at a research centre into equipment that can be manufactured, installed and operated reliably in production environments. The round’s focus on industrialisation and deployment addresses that transition directly: more manufacturing capacity and a more robust, scalable system can make it easier to repeat installations across food-processing lines rather than treating each deployment as a one-off technical project.
REDIT Ventures’ Investment Club adds an industry channel as well as capital. REDIT says the club brings together executives and entrepreneurs linked to the Valencian technology institutes, giving ART Technologies access to strategic support and an industrial network. That connection may help the company reach potential customers and partners, but the next proof point will be converting that access into repeatable deployments. The system has to fit the speed, product variability and quality-control routines of food plants while producing information operators can use during production.
