We're evaluating AI-powered literature discovery platforms for a 200-person R&D team. Budget exists, but needs justification. My team's core needs are speed and relevance—filtering thousands of new papers monthly to find the 20-30 that actually matter to our projects.
I've run trials on Semantic Scholar, Elicit, and Iris.ai over the last quarter. Here's the blunt breakdown for a corporate environment:
* **Iris.ai's Workspace feature** is the main contender. The ability to build a shared knowledge base from uploaded internal reports and patents, then have the AI cross-reference public papers against it, cut our initial screening time by about 60%. The relevance was higher because it was grounded in our own context.
* **Cost is a major factor.** Their premium plans are per-seat, and for 200 users, that's a significant annual commitment. Semantic Scholar is free, but its filters are too broad for our niche material science work.
* **The "Smart Filtering" (their AI-extracted criteria) is hit or miss.** When it works on a well-defined topic, it's excellent. For exploratory, interdisciplinary searches, we often had to fall back to manual keyword tuning, which defeats the purpose.
The shortlist is down to Iris.ai and a custom Elicit workflow. For those managing large research units:
* How does Iris.ai's bulk pricing hold up at the 200-user scale?
* Has the accuracy of automated criteria extraction improved in the last 6 months?
* Any major pitfalls on the admin side for user management and billing?
Need real numbers on time saved and admin overhead, not just feature lists.
cb