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Parter Selected for ICON's SV101 Silicon Valley Program

Being selected for SV101 builds on our momentum as the AI platform where hardware design meets production reality.

Parter Selected for ICON's SV101 Silicon Valley Program

I'm excited to share that Parter has been selected for Batch 19 of SV101, a ten-day program run by ICON. We're one of 11 companies chosen for this batch, which connects Israeli founders with Silicon Valley investors, executives and enterprise leaders.

Getting in is not easy. Fewer than 10% of qualifying applicants make it into each batch, after a finalist pitch day in front of leading U.S. and Israeli investors and interviews with program alumni. About 31% of the startups that have gone through SV101 since 2016 have had an exit. Being selected by this community of investors and operators is a strong validation of what we've built, and I want to thank the ICON team, the judges and the alumni who took the time to get to know us.

Batch 19 runs October 12-22, 2026 in Silicon Valley. Over those ten days, we'll meet one-on-one with investors and technology executives, learn from founders who have scaled companies in the U.S., and get direct feedback as we grow.

The selection comes at a strong moment for us. We recently launched our BOM Optimizer, which optimizes a bill of materials in one click for cost, second sources, lead times, end-of-life parts and shortage risk. Our customers see over 14% average cost reduction on components.

Hardware teams are being asked to move at software speed, but the supply chain hasn't caught up. We saw this firsthand scaling hardware companies through the COVID-19 shortages, when a single missing part could stop a production line. Every part decision made in design shows up months later as a cost, a shortage or a redesign. We built Parter so those problems surface early, while they are still cheap to fix.

We take a different approach. Our platform connects design to the supply chain, giving R&D and supply chain teams real-time visibility and alerts from early prototypes to mass production. Instead of reacting to problems on the production line, teams can optimize their products early and move to production faster.

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