If you've ever spent 3 hours searching for a paper you know exists, or scrolled through 40 irrelevant results to find the one dataset that actually matters — you already know the problem.

Traditional search engines are built for consumers, not researchers. They optimize for clicks, not comprehension. They surface popular results, not precise ones.

What researchers actually need:

• Semantic understanding of complex queries ("papers on CRISPR off-target effects in neural tissue published after 2022")

• Cross-reference discovery — finding connections between documents you didn't know were related

• Deep retrieval across formats: PDFs, datasets, preprints, patents, and institutional repos

That's exactly what SearchX does. We built an AI search model from the ground up that understands research context, not just keywords.

No more boolean operators. No more manual filtering. Just describe what you need in plain language and let the model do the heavy lifting.

We're live now and taking early members. Would love feedback from anyone deep in research workflows.