Building hardware today is harder than it should be. Data is scattered. Information is outdated or incomplete. Teams waste valuable time chasing datasheets or cleaning spreadsheets instead of making confident decisions.

We saw this firsthand. As engineers founders and operators we kept running into the same wall. We couldn’t get clean answers to basic questions about the components we were using. Is this part available. Will it be around in three years. Is there a better option that’s more reliable cheaper or easier to source.

The Limits of Existing Tools

Existing tools didn’t help. ERP and PLM systems are not built for speed. Distributors offer basic search. Teams fall back to tribal knowledge and manual work. Mistakes happen and they’re expensive. Especially in hardware.

A Different Approach to Component Data

That’s why we built Parter. We set out to create an AI native platform designed for the realities of hardware development. Our goal is to bring structure to messy data and give teams instant access to the insights they need.

Parter connects scattered information from datasheets supplier feeds lifecycle notices and BOM spreadsheets. We apply AI to extract normalize and enrich every part of the component data stack. Then we surface risks insights and alternatives directly where engineers and supply chain teams need them.

We’re not here to replace human judgment. We’re here to support it. To make it faster more accurate and more scalable.
This is just the beginning. The future of hardware requires better data. We’re building the foundation.