scite.ai vs Iris.ai

Scite helps research teams ask literature questions and get answers tied to full-text papers, not abstract snippets alone. Its Assistant searches across 280M+ full-text scholarly articles, and Smart Citations show whether later work supported, contradicted, or mentioned a finding.

General research chatbots guess when paywalls block the PDF. Scite licenses content from Wiley, SAGE, and 40+ other publishers, so it reads inside the paper and links each claim to a specific sentence you can verify in one click. That evidence-first design targets literature reviews where you need to know if a result still holds, not just that it exists.

Grad students run disputed-or-affirmed checks before citing a paper. R&D teams trace grants, clinical trials, and patents in one search. Pro users get API access, 2,500 MCP credits per month, and collections up to 10,000 papers. Scite also plugs into Claude, ChatGPT, and Zotero so answers show up inside existing workflows.

Iris.ai builds enterprise knowledge platforms that turn scattered PDFs, patents, reports, graphs, and tables into structured data for RAG pipelines and AI agents. Its Axion product extracts multilingual documents across 68 languages, while Neuralith and RSpace target agentic workflows and complex R&D intelligence.

General-purpose RAG tools often choke on regulated industries where contracts, clinical trials, and patent libraries need schema-aware extraction and audit trails. Iris.ai ships ISO 27001-certified processing with GDPR compliance, on-premise deployment, and a built-in LLM evaluation layer so teams in pharma, telecom, and manufacturing can trust what their agents retrieve.

Research teams, legal ops, and innovation groups at enterprises like ArcelorMittal use Axion to cut patent review time by up to 90% with 94% extraction precision. Pricing runs from a Starter one-time pack for 10 documents through Professional tiers with about 100k pages to Enterprise deals from roughly €0.05 per page.