Causaly
Leverage the power of AI to expedite the journey from bench research and laboratory insights to the launch of life-changing therapies. Gain up to 90% in research productivity by reducing your reading time from months to minutes. Cut through the noise with a high-precision, high-accuracy search to navigate the ever-growing volume of scientific literature with ease. Save time, reduce bias and increase odds of novel discoveries. Deeply explore disease biology and conduct advanced target discovery. Causaly’s high-precision knowledge graph consolidates evidence from millions of publications, making deep, unbiased scientific exploration possible. Rapidly navigate biological cause-and-effect relationships without being an expert. Get a view of all scientific documents and uncover hidden connections. Causaly’s powerful AI machine reads millions of published biomedical literature to support better decision-making and research outcomes.
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ScholarIQ
ScholarIQ is a web-based AI research assistant for searching and reviewing scientific literature. Researchers can pose a question, search records from OpenAlex, ORCID and PubMed, or upload search results and source documents. The platform ranks articles against review criteria, supports citation-based snowball discovery, and helps users screen records, highlight relevant passages, and extract specified information from uploaded texts. Answers and suggested values link to cited sources, while reviewers retain control of inclusion decisions, risk-of-bias judgments, and conclusions. ScholarIQ records reviewer identity, decision rationale, and supporting passages to make review work traceable. It also provides a public research metadata atlas linking papers, researchers, journals, institutions, and topics. It serves individual researchers and review teams doing literature searches, evidence screening, and systematic reviews.
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SciSpace BioMed Agent
SciSpace BioMed is a domain-native AI “co-scientist” for biomedical research that combines a vast literature database with 150+ integrated bio-tools and 100+ academic databases and software suites to streamline complex research workflows, from genomics and single-cell analysis to drug discovery and clinical genomics. It enables researchers to ask natural-language questions, ingest datasets, interpret variants or multi-omics data, design cloning or wet-lab workflows, reason about clinical or disease biology, and generate publication-ready outputs (e.g., figures, tables, and presentations) with full transparency and citations. Users can interact with scientific papers via “chat with PDF,” highlight confusing text, math, or tables, and get clear explanations, ideal for understanding difficult methods or concepts. For literature review or exploratory research, its AI-driven semantic search accesses millions of papers and returns citation-backed summaries.
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CiteDash
CiteDash is an AI-powered research and writing platform designed to streamline the entire academic workflow by combining source discovery, analysis, drafting, and citation into a single system. It enables users to enter a research topic, essay prompt, or question, after which a multi-agent pipeline automatically searches across numerous academic databases, including sources like Semantic Scholar, PubMed, and OpenAlex, to identify, evaluate, and synthesize relevant literature into a structured draft with inline citations. It emphasizes accuracy and credibility by grounding every claim in verifiable academic sources, avoiding hallucinated references, and ensuring that outputs are traceable to real studies. CiteDash supports a wide range of academic tasks, including essay writing, research papers, literature reviews, and exam preparation, offering tools such as AI-generated notes, structured outlines, and active recall question generation to reinforce learning.
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