Plans for people who don't like the same things.

Tell us what everyone loves. An agent maps each person onto Qloo's taste graph, scores every option against every person, and plans the one night everybody actually enjoys.

Name a few things each person loves: bands, films, shows, books, food, brands.

How it finds common ground

  1. 1Resolve. Each person's loves become real entities in Qloo's 250M+ entity taste graph: bands, films, books, places, brands.
  2. 2Explore. For every slot of the plan, the agent asks Qloo what the whole group, and each person alone, would gravitate to.
  3. 3Score fairly. Every candidate gets a fit score per person. Picks are ranked so nobody is left out (maximin), not just by the average.
  4. 4Explain. Gemini writes the plan, citing the taste evidence for each person, and is only allowed to use entities Qloo returned.

Why not just ask a chatbot? An LLM alone can name popular places, but it can't measure how much a Wong Kar-wai fan and a Fast & Furious fan will each enjoy a specific restaurant. Each plan includes a control experiment so you can compare.

Give your own agent taste

Common Ground is also an MCP server. Plug it into Claude, Cursor, ChatGPT or Antigravity and any agent can call taste_overlap whenever it recommends something to more than one person.

claude mcp add --transport http common-ground https://your-app.vercel.app/api/mcp