Perhaps the largest-ever Hungarian–Israeli Business Forum brought together two complementary innovation cultures: Hungary’s industrial–engineering base and Israel’s R&D-driven dynamism. The event featured ministerial-level participation—attended by Péter Szijjártó, Hungary’s Minister of Foreign Affairs and Trade, and Gideon Sa’ar, Israel’s Minister of Foreign Affairs. In total, 62 Hungarian and 36 Israeli companies took part, and 150+ B2B meetings were held in a single day. The strong diplomatic presence—and the numbers themselves—show that political messages have translated into tangible, project-level collaborations.

The InnovITech team—just as at other flagship events of the domestic ecosystem—was on site again, holding talks with major Israeli corporations, defense and cybersecurity stakeholders, as well as government and diplomatic leaders.

Discussion Topics

  • Cybersecurity / Critical Infrastructure
  • Healthcare / Data and AI in practice

Two areas were especially productive with Israeli health‑tech partners:

    • Early diagnostics and support for personalized therapy. Through ML‑based correlation analysis across functional genomics + imaging + clinical data—as developed in InnovITech’s GENEFXplore project—we aim to support early diagnosis and therapy selection.
    • Microbiome and clinical data correlations; 3D genome visualization. Our MICROBIOLANCE project—which analyzes relationships between microbiome and clinical profiles—attracted strong interest, as did GenomeAtlas (3D genome‑structure visualization). These are data‑ and AI‑intensive examples of how we translate research results into clinical decision support.
  • AgriTech / Precision Farming

With agricultural partners, the focus was on field data collection and robotics. We presented our GRAPLER vineyard‑pruning robotics development.

    • GIS‑based block/row/plant‑level records and intervention tracking
    • Robotics / Vineyard‑pruning platform: A 3D reconstruction approach to pruning; application of digital‑twin solutions; VR‑based pruning support; ML‑based autonomous rover movement; and pruning‑vector determination—all of which drew significant interest.