Streamline decision-making with AI-driven consensus.
Grade: A — Score: 90/100
Consensus utilizes advanced AI technology to analyze and synthesize information from various sources, providing users with actionable insights to make informed decisions quickly.
The platform enhances workflow by enabling teams to collaborate seamlessly, ensuring that all voices are heard and considered in the decision-making process, ultimately leading to more democratic outcomes.
However, organizations must be aware of potential risks, such as over-reliance on AI-generated insights and the need for human oversight to validate decisions made based on automated recommendations.
Free: Free
Premium: $11.99/month ($8.99/month billed annually at $107.88/year)
Team: $12.99/person/month ($10/person/month billed annually at $120/person/year)
Enterprise: Custom pricing (200+ users)
Consider switching to Miro: Miro offers similar collaborative features but focuses more on visual brainstorming.
Consensus searches exclusively peer-reviewed scientific literature (200M+ papers), while Perplexity AI searches the general internet and Google Scholar indexes academic papers without AI synthesis. Consensus's key differentiator is that it reads and synthesizes findings across multiple papers, providing an AI-generated summary with citations rather than just a list of links. The Consensus Meter (showing scientific agreement on yes/no questions) has no equivalent in Perplexity or Google Scholar. However, Perplexity covers a far broader range of sources (news, web, forums) and Google Scholar provides direct access to full-text papers through institutional subscriptions. Consensus is best for evidence-based answers from science specifically; Perplexity is better for general research; Google Scholar is better for finding specific papers by title, author, or DOI.
The Consensus Meter is a visual indicator that shows the level of scientific agreement on yes/no research questions. When you ask a question like 'Does creatine supplementation improve athletic performance?', Consensus analyzes the findings from relevant papers and classifies each as supporting 'Yes', 'No', or 'Possibly'. The results are displayed as a percentage bar showing how much of the evidence agrees or disagrees. This feature is designed for questions that have a directional answer — it works best for empirical questions in health, nutrition, psychology, and policy. For open-ended or exploratory questions, the standard AI search and Pro Analysis are more appropriate. The Consensus Meter is available on all plans including Free.
Consensus searches over 200 million peer-reviewed academic papers sourced from Semantic Scholar, OpenAlex, and its own web crawls. Coverage spans a wide range of scientific disciplines including medicine, biology, social sciences, psychology, economics, environmental science, and more. According to independent analysis, Semantic Scholar (a primary data source) has one of the broadest coverage rates across disciplines, second only to Springer Link. Consensus does not search non-academic sources like news articles, Wikipedia, industry reports, blogs, or patents. The platform performs best for medical and social policy questions according to its own documentation, though it covers most scientific fields.
Yes. The Ask Paper feature allows you to interact directly with the full text of any research paper in the database or upload your own PDF documents. You can ask specific questions about methodology, data points, conclusions, or any section of the paper and receive AI-generated answers grounded in the document's content. This is particularly useful for quickly extracting key details from dense papers without reading them cover-to-cover. Ask Paper is available on the Premium plan ($11.99/month or $8.99/month annual). The Free plan does not include this feature.
Yes — citation is a core design principle of Consensus. Every AI-generated summary, insight, and analysis includes direct, clickable citations to the original research papers. Users can click through to view the paper abstract, metadata, and (where available) full text. Citations can be exported to reference managers including Zotero, Endnote, and Mendeley. This citation-first approach distinguishes Consensus from general-purpose AI tools like ChatGPT, which may generate plausible-sounding but unverifiable claims. Every answer in Consensus is traceable back to published, peer-reviewed research.
Yes. Consensus offers a 40% discount on the Premium plan for users with a verified educational email address (.edu or equivalent academic domain). This brings the annual cost down to approximately $65/year (roughly $5.40/month), making it one of the most affordable AI research tools for students and academics. The Team plan for research groups is priced at $12.99/person/month ($10/person/month annual). Enterprise pricing is available for universities and institutions with 200+ users and includes custom deployment and volume licensing.
Yes. Consensus provides a free plan that includes basic AI search across its full 200M+ paper database, the Consensus Meter for yes/no questions, up to 25 Pro Analyses per month, limited Study Snapshots, citation export, and basic library features. The free tier is genuinely usable for occasional research and evaluation — there is no time limit on the free plan. Premium features like Deep Search, Ask Paper, Medical Mode, and unlimited analyses require the Premium plan at $11.99/month ($8.99/month annual). No credit card is required to sign up for the free plan.
Consensus was founded in 2021 by Christian Salem and Eric Olson, two former Northwestern University Division 1 football teammates from families of researchers and teachers. Their mission is to make scientific research accessible to everyone — ensuring 'no one feels like an outsider to science.' Eric Olson (CEO) previously worked in data science at DraftKings and holds a Masters in Predictive Analytics from Northwestern. The company is based in San Francisco and has raised $19.2M in total funding, including an $11.5M Series A in 2024 led by Union Square Ventures with participation from Nat Friedman (former GitHub CEO) and Daniel Gross. The team is described as small and scrappy — approximately 6–15 people — focused on engineering and science.