COMMON MAPS COMMON MAPS We build activity maps of complex organizational landscapes using AI. Find patterns in organization activities, funding flows, relationships, and more.
A finished map organizes every organization in a landscape by what it actually does. Below is one branch of our New York demonstration, layer by layer, with real numbers. New York is just one landscape: the engine builds a map like this for any set of organizations.
In every map, organizations are organized by the activities they perform. In the New York map, those activities are grouped into three levels, and the topmost level has 27 broad groups. Let's dig deeper into Youth Development.
Within each top-level group sit more specific groups. Under Youth Development, those include things like After-School and Extended Day Programs and Student Activities and Campus Life. Let's take a look at Camps and Outdoor Programs.
Camps and Outdoor Programs has subcategories of its own. Let's dig into Summer Day Camp Programs and look at the organizations doing that work.
Groups collect activities, not whole organizations. An organization usually does many things, so it appears in every group where it performs that kind of work. All 455 organizations here run a summer day camp; most of them show up in other groups across the map too. Let's open one.
An organization's detailed view shows every activity it performs across all the groups it is active in, plus its published financials, relationships, populations served, and much more.
A summer arts camp serving students from kindergarten through 12th grade, with immersive instruction in music, dance, theater, visual arts, and nature and sustainability on a woodland campus.
Children and youth in grades K–12 · Wheatley Heights, NY
Those same details support analysis across the whole map: financial flows, populations served, and other patterns compared between activity groups, at landscape scale.
Every organization in a landscape is researched across the public record: its filings, its own site and documents, and what others publish about it.
AI organizes what it finds into activities, strategies, funding flows, populations, and relationships, each traceable back to its evidence.
Explore the map visually, read organization dossiers, ask questions in plain language, or connect your own tools through the API and MCP.
Nothing in the engine is specific to nonprofits. It takes two inputs: a list of organizations and their public evidence, whatever form that takes. The nonprofit sector is the current demonstration because its public record runs deepest, with IRS 990 filings underneath everything organizations publish about themselves.
Applied to publicly traded companies, the same pipeline produces peer groups far finer than standard sector classifications: groups tight enough to compare what close competitors actually do, how their activities differ, and how those differences move together. The refinery and the instrument stay the same. Only the evidence changes.
61,800+ organizations, mapped by what they do.