This review is part of a larger series of LinkedIn newsletters titled The Human Side of AI: Cutting through the AI noise to show you how AI can be a powerful tool for your creativity, efficiency, and strategy.
Roundtable AI puts Claude, ChatGPT, Grok, and Gemini in one thread. Ask a question with @all and four models answer side by side. @mention one for a single take. It behaves less like a chatbot and more like a room where you watch four advisors agree or argue before you commit.
The team is direct about scope. Roundtable is built for questions worth five figures and weeks of work, and it says outright that everyday prompts belong in a single tool.
Key strengths
Pressure-testing a questionnaire before it fields
Fan a draft screener or discussion guide to @all and the models rarely flag the same problems. One flags a leading stem, another an unbalanced scale. Where they disagree is usually where the question is ambiguous, which is the spot a researcher wants to find before fielding rather than after.
Triangulated desk research with a paper trail
The same competitive or sizing question run across four providers exposes where a single model quietly invents a number or a competitor. Deep Research, on the Standard plan, runs multi-step web investigations and returns cited reports. Decision Trails let you branch a question into versions and export clean Markdown, so how a finding was reached is documented rather than remembered.
Stress-testing a recommendation
Before a recommendation lands in a deck, a Devil’s Advocate custom agent and an @all consensus check surface the objection a client will raise. Agreement across providers builds confidence, and disagreement is the cue to gather more evidence. @secretary then turns the exchange into a summary and action items ready to drop into a deliverable.
Where it could improve
The gap that matters most for research is volume qual work. Roundtable is built around typed questions, and no document-upload feature appears in its publicly available marketing or security materials, so coding twenty interview transcripts or categorizing hundreds of open ends is not what it is for. NotebookLM or a direct model with file upload still owns that job.
Cost is the other friction. Lite runs $30 a month, but Deep Research and the premium models sit on the $200 Standard plan. On data handling, conversations are excluded from provider training by default and encrypted at rest, but there were no publicly listed SOC 2 or ISO certifications at the time of writing. The company says it will sign a DPA on request. For client transcripts under NDA, that is worth a procurement conversation before anything sensitive goes in.
Ratings
| Dimension | Rating | Rationale |
|---|---|---|
| Usability | 4.0 / 5 | No API keys and plain-language @mentions get a researcher going quickly; the agent and syntax layer takes a little learning. |
| Power | 3.5 / 5 | Strong on question design, triangulation, and Decision Trails; capped by the absence of confirmed transcript-level qual analysis. |
| Flexibility | 3.0 / 5 | Covers several research-adjacent tasks and exports to Markdown, but no confirmed document upload and a focus on human-in-the-loop agents rather than autonomous workflows. |
| Cost | 3.0 / 5 | Lite is fair at $30; the research-grade tier is $200 a month, justified only when the decision on the table is genuinely high-stakes. |
Conclusion
Roundtable AI earns its place on one specific job: the moment a researcher needs more than one confident answer. Watching four models converge or split is a fast way to see where a question, a number, or a recommendation is soft. It will not code your transcripts or replace the analyst reading them. What it does is put four perspectives in front of the human making the call, which is where the judgment still lives.
Last updated: 8/6/2026.