Alright folks, gather 'round. I've been down in the home lab, tinkering with the usual suspects—Prometheus alerts, Ansible playbooks, you know the drill—but my latest side quest is helping a buddy's tiny startup. Three people, brilliant minds, drowning in academic papers. They're building something in the biotech space, and keeping up with the literature is a part-time job they don't have.
We tried the usual: Google Scholar alerts, manual Zotero libraries, a chaotic shared Notion page. It was like herding cats… during an on-call incident. 😅
So we started looking at these AI literature tools. You know the ones that promise to "revolutionize your research." But here's the rub: most are built for massive institutions with deep pockets, or they're glorified keyword matchers with a fancy UI.
My buddy's startup needs:
* **Actually smart filtering** – Beyond keywords, finding connections they might miss.
* **Collaboration that doesn't suck** – Three people, one truth. Not three conflicting libraries.
* **A price tag that doesn't require VC funding** – We're talking "monthly cloud bill" budget, not "enterprise license."
* **PDF handling that isn't a pain** – Upload a bunch of existing PDFs and get something useful back.
We gave Iris.ai a spin for a few weeks. I'll be honest, the "Research Space" concept is pretty neat for a small team. It feels like a shared evidence board. But I'm curious about the real-world, scrappy startup experience.
Has anyone here, especially in a small team or a home-lab-like setup (where every dollar counts), actually integrated something like Iris.ai into a daily workflow? Does it *actually* save time, or just create a new system to manage?
I'm particularly interested in the "Contextual Search" and "Extract" features. Are they finding those "aha!" connections, or is it just a more expensive way to find the same papers we'd get with a well-crafted boolean search on PubMed?
Bonus points if you've compared it to other tools like Litmaps or even some of the open-source offerings (though my time for self-hosting a complex NLP pipeline is… limited these days). Let's hear your war stories.
-- Dad
it worked on my machine