STATION Ai Hosts Reverse & Answer Pitch Event
On November 4, 2025, STATION Ai, a prominent open innovation hub in Japan, is set to host a collaborative Reverse & Answer Pitch event. This event is part of the second term of its paid support program, SKIP Focus. Targeted at partner companies within the STATION Ai ecosystem, the event promises to be a vibrant venue for showcasing innovative solutions and establishing meaningful connections between startups and established businesses.
Event Details
STATION Ai, which is headquartered in Nagoya with Hirotaka Sagawa at its helm, is committed to facilitating collaboration and innovation among startups and established corporations. This event will take place from 12:30 PM to 3:30 PM at the event space on the first floor of the STATION Ai building.
Interested parties can apply to participate as either startups presenting in the Answer Pitch section or as audience members to listen to Reverse Pitches. The periods for application submissions are as follows:
- - For startups wishing to pitch: October 7 to October 19.
- - For startups and audiences wanting to attend: October 7 to October 30.
It’s important to note that due to limited spots, the registration may close before the deadlines if the maximum capacity is reached.
About the SKIP Focus Program
SKIP Focus is an innovative support program where OI coordinators at STATION Ai work closely with partner companies to develop open innovation strategies from conception through execution. Participating startups undergo a thorough analysis of their challenges and set objectives tailored for their collaborative projects, culminating in Reverse Pitches to attract suitable partners.
Participating Companies and Themes
Two notable companies presenting at the event are:
1. NETZ TOYAMA Co., Ltd.
- - Pitch Theme: Creating a new car sales model that uses AI to understand customer insights, enabling a swift and satisfying purchasing experience.
- - Technological Needs/Collaborator Requirements:
- An AI avatar that interacts with customers based on their purchase history and preferences to deliver tailored recommendations, termed