# Frequently asked questions > Frequently asked questions about LLM Peers: multi-model deliberation, intake, web research, privacy, sharing, decision briefs, costs, and when to use the board. Canonical HTML: https://llmpeers.com/faq Answers about what LLM Peers is, how a run works, how privacy and sharing behave, and when the board is a better fit than single-AI chat. If you need something not covered here, contact Version Labs directly. ## Product basics LLM Peers is built for consequential decisions: multiple models research independently, peer-rank anonymized answers, and a chairman synthesizes one recommendation. It is decision support—not a replacement for professional advice or your judgment. ### What is LLM Peers? LLM Peers is a multi-agent research platform. Multiple AI models answer independently, peer-rank anonymized responses, and a chairman synthesizes one decision-ready recommendation with risks, assumptions, and next actions. ### How is this different from ChatGPT? ChatGPT optimizes for helpful single-model conversation. LLM Peers runs a structured deliberation: independent answers, anonymous peer review, and a final decision brief for consequential choices. Many teams use both. ### How is this different from Perplexity? Perplexity is excellent for fast, source-grounded research answers. LLM Peers focuses on deliberating options across models and producing a ranked decision brief with visible agreement and dissent. ### Who is LLM Peers for? Founders, operators, product and engineering leaders, analysts, procurement teams, and professionals who need structured multi-model research before committing to a consequential choice. ### Do I have to answer intake questions? No. Intake appears when clarifying context would improve the research brief. You can answer or skip and continue into the council run. ### Does LLM Peers search the web? Yes, when web research is enabled for a run. The board can gather a current briefing so deliberation is not limited to model training knowledge alone. ### What is a decision brief? The chairman synthesis at the end of a run: a clear recommendation, ranked options, agreement and dissent, key assumptions, risks, and a practical next action you can take or challenge. ### What happens during peer ranking? Council answers are anonymized and evaluated against shared criteria so review focuses on substance rather than which model wrote which answer. Rankings then inform the final synthesis. ### Is my research private? Research in your account stays private unless you explicitly publish a public share link. See the Privacy Policy for details on account data, subprocessors, and retention. ### Can I share results with my team? Yes. After a completed run you can publish a public research page so others can read the question and decision brief through a share URL. Only publish content you are comfortable making public. ### Can public shares be indexed by search engines? Published share pages may be accessible to anyone with the URL and may be indexed. Unpublish or avoid sharing sensitive material you do not want public. ### Does this replace professional advice? No. LLM Peers supports judgment. It does not replace licensed legal, medical, financial, or regulatory advice where qualified expertise is required. ### How long does a run take? Timing varies with intake, web research, model load, and question complexity. Runs are progressive: you can watch stages complete rather than waiting for a single black-box response. ### What models are used? LLM Peers orchestrates multiple models in a council workflow. Exact model lineups can change as the product and configuration evolve; the constant is independent answers, peer review, and synthesis. ### Who builds LLM Peers? LLM Peers is built by Version Labs. Contact contact@versionlabs.co for support, privacy requests, or partnership inquiries. ## When to use the board Use LLM Peers when trade-offs are real and a chat transcript is not enough of a handoff. Use a general assistant when you need speed for drafting, coding help, or low-stakes exploration. - Strategy, pricing, and go-to-market choices - Build vs buy vs partner decisions - Vendor shortlists and procurement trade-offs - Roadmap prioritization with stakeholder conflict - Diligence-style go/no-go questions ### More detail - [How it works](https://llmpeers.com/how-it-works): Full run walkthrough - [Privacy](https://llmpeers.com/privacy): Data handling overview - [Contact](https://llmpeers.com/contact): Ask us directly - [vs ChatGPT](https://llmpeers.com/compare/chatgpt): Detailed product comparison - [Features](https://llmpeers.com/features): Council, briefs, sharing, and more ## Related pages - [Features](https://llmpeers.com/features) - [Benefits](https://llmpeers.com/benefits) - [About](https://llmpeers.com/about) - [Use cases](https://llmpeers.com/use-cases) --- Publisher: Version Labs · Product: LLM Peers