IBM Watson Discovery
Find specific answers and trends from documents and websites using search powered by AI. Watson Discovery is AI-powered search and text-analytics that uses innovative, market-leading natural language processing to understand your industry’s unique language. It finds answers in your content fast and uncovers meaningful business insights from your documents, webpages and big data, cutting research time by more than 75%. Semantic search is much more than keyword search. Unlike traditional search engines, when you ask a question, Watson Discovery adds context to the answer. It quickly combs through content in your connected data sources, pinpoints the most relevant passage and provides the source documents or webpage. A next-level search experience with natural language processing that makes all necessary information easily accessible. Use machine learning to visually label text, tables and images, while surfacing the most relevant results.
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Charlie
Charlie is a sovereign AI scientific agent for researchers and laboratories, specialized in biomedical research and built to accelerate scientific work with precise, sourced answers. It helps users conduct literature reviews, analyze scientific documents, organize research information, and move faster through R&D workflows while keeping traceability at the center of every response. Researchers can ask questions in natural language and get answers extracted directly from scientific documents, with page-level citations that make each source verifiable. Charlie can search simultaneously through hundreds of PDFs, synthesize information, compare articles, understand scientific context, and help users focus on discoveries instead of manual document review. Its research workspace includes libraries, projects, notes, shared collections, PDF reading, highlighting, annotation, and team collaboration, so important passages, references, and insights stay organized across devices.
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Sci-Bot
Sci-Bot is an AI-powered research assistant built on top of Sci-Hub, designed to answer user questions by directly searching and analyzing scientific literature instead of generating responses from general training data. It works by scanning a vast database of full-text academic papers and extracting relevant findings to produce answers grounded in published research, often including references and links to the original studies. It presents itself as a simple interface where users can ask questions in multiple languages, and the system automatically searches for relevant keywords, retrieves academic articles, and synthesizes information into a structured response. It follows a step-by-step process similar to a human researcher, showing intermediate searches and sources before delivering a final answer. A key focus is on reducing hallucinations by relying on existing scientific papers rather than purely generative outputs, and it can provide direct access to full-text documents.
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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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