We are pleased to announce that TD Shepherd acts as the strategic advisor to onsite.ai to support the capital raise. Onsite.ai is an AI-based technology startup founded in Germany in 2022, which brings forth automated, AI-powered transportable laboratories for quantitative analysis of air samples to detect mold spores, pollen, soot & others. This cutting-edge technology solution will make a real impact in the world of immediate air quality analysis.
As strategic advisors, TD Shepherd is working closely with onsite.ai to identify and implement new strategies which will help to expand their reach and succeed in fundraising. Our deep tech experts define and manage the fundraising process forward all the way to the term sheet and shareholders agreement.
From determining the ambition to completing all the preparations necessary to start a financing round, we provide valuable support and guidance to the onsite.ai team to achieve their goals and establish/ strengthen their position in the market.
TD Shepherd has a proven track record of success in helping deep tech startups like onsite.ai reach their full potential. We are excited about this partnership with onsite.ai and look forward to taking the project to new heights.
Semiconductor innovation requires navigating a complex maze of high costs, lengthy development and qualification paths, detailed technical specifications, and, of course, the right market timing. For startups that seek to lead in their space, external expertise isn’t just helpful; it is almost always essential.
We are excited to announce TD Shepherd as a partner of High-Tech Venture Days 2025 (HTVD25). HTVD is a matchmaking event that brings together three main groups: startups, investors (especially venture capital firms, corporate venture arms, etc.), and established companies (corporates).
Generating infrared light isn’t difficult. But generating it on a chip — reliably, efficiently, and compactly — is a different story. Traditional IR sources rely on heating elements. They radiate across a wide range of wavelengths, consume significant power, and require filters to isolate the signals. Manageable in labs, unworkable in wearables.