SciTransfer
Organization

INGELUX

French SME bridging LED photonics expertise and AI-powered digital twins for industrial lighting infrastructure in Industry 4.0 environments.

Technology SMEdigitalFRSMEThin data (2/5)
H2020 projects
2
As coordinator
0
Total EC funding
€403K
Unique partners
34
What they do

Their core work

INGELUX is a French technology SME based near Lyon that specializes in LED lighting systems — from precise measurement and standardized compact modeling of LED components to AI-driven digital twins for industrial lighting infrastructure. In their earlier EU work they contributed to developing multi-domain compact models of LEDs that translate physical measurements into standardized engineering tools used across the industry. More recently, they have moved into the digitalization of the entire lighting design and maintenance lifecycle, applying artificial intelligence and digital twin methods to predict LED lifetime and optimize lighting infrastructure in Industry 4.0 factory environments. Their value to a consortium lies in combining hands-on photonics and lighting hardware expertise with an emerging capability in data-driven, model-based engineering.

Core expertise

What they specialise in

LED component modeling and standardizationprimary
2 projects

DELPHI4LED (2016–2019) focused directly on translating LED measurements into standardized multi-domain compact models, a foundation that underpins their later digital work in AI-TWILIGHT.

Digital twin for lighting infrastructureprimary
1 project

AI-TWILIGHT (2021–2025) is explicitly built around an AI-powered digital twin for lighting infrastructure in front-end Industry 4.0 contexts.

LED lifetime prediction and reliabilitysecondary
1 project

AI-TWILIGHT lists 'lifetime prediction' as a core keyword, indicating INGELUX contributes predictive reliability methods for lighting systems.

Digitalised design flow for lighting productsemerging
1 project

AI-TWILIGHT keywords include 'digitalised design flow', suggesting INGELUX is developing competence in model-based and AI-assisted product design pipelines.

Evolution & trajectory

How they've shifted over time

Early focus
LED measurement and compact modeling
Recent focus
AI digital twin for lighting

In their first project (DELPHI4LED, 2016–2019), INGELUX worked at the measurement and standardization layer — the challenge of capturing LED behavior in compact models usable across the industry. No AI, no digital twin; the focus was physics-accurate characterization. By their second project (AI-TWILIGHT, 2021–2025), every keyword had shifted: digital twin, AI, Industry 4.0, lifetime prediction, digitalised design flow — language from the systems integration and smart manufacturing world. The trajectory is clear: they have moved from component-level metrology toward end-to-end digital infrastructure management, using their deep LED knowledge as the physical foundation for higher-level AI and digital twin systems.

INGELUX is moving steadily up the value chain — from measuring LEDs to modeling them, and now to building AI systems that manage entire lighting infrastructures in smart factories, making them an increasingly relevant partner for Industry 4.0 and smart building projects.

Collaboration profile

How they like to work

Role: specialist_contributorReach: European11 countries collaborated

INGELUX has participated exclusively as a consortium partner — never as coordinator — across both projects. Their involvement in an ECSEL-RIA project (AI-TWILIGHT) places them alongside large industrial players, chip manufacturers, and research institutes typical of European semiconductor and electronics consortia, which tend to be large (15–30+ partners). With 34 unique partners across just 2 projects, they engage deeply in wide networks rather than working in tight, repeated bilateral arrangements. This suggests they are comfortable operating as a specialist node in complex multi-stakeholder programs, contributing a defined technical slice rather than driving the overall agenda.

Despite only two projects, INGELUX has worked with 34 unique partners across 11 countries — an unusually broad network for an SME at this scale, explained by their participation in ECSEL-RIA, which typically assembles large pan-European industry-academia consortia. Their geographic footprint spans at minimum the core EU innovation corridor (France, Germany, Benelux, and likely Southern and Northern Europe given ECSEL composition).

Why partner with them

What sets them apart

INGELUX occupies a rare intersection: a small French company that understands LED physics at the component level and is now applying that knowledge inside AI and digital twin systems for industrial environments. Most digital twin players come from the software or automation side and lack deep photonics grounding; most LED specialists stay in the hardware and optics domain. INGELUX bridges both, which makes them a credible partner for projects that need someone who can translate between the physical behavior of light sources and their digital representation in smart factory systems.

Notable projects

Highlights from their portfolio

  • AI-TWILIGHT
    Their largest and most recent project (€238,990, running to 2025), combining AI, digital twin technology, and Industry 4.0 for lighting infrastructure — the clearest signal of where INGELUX is heading and what they can bring to future consortia.
  • DELPHI4LED
    A foundational European standardization effort for LED compact modeling that established INGELUX's credibility in the photonics and lighting community and gave them the measurement expertise underlying their later digital work.
Cross-sector capabilities
Manufacturing / Industry 4.0 — lighting infrastructure in smart factory environmentsEnergy efficiency — LED system optimization and lifetime extensionSmart buildings and urban infrastructure — intelligent lighting management
Analysis note: Profile is based on only 2 projects; the early project (DELPHI4LED) has no recorded keywords in the dataset, so the evolution analysis relies partly on the project title and description rather than structured keyword evidence. No website was available for additional context. The profile is directionally reliable but would benefit significantly from a third data point or direct organizational information.