This report was built on the graph's new self-describing layer — so it didn't start with a list of what to look up. It asked the graph what it knew, then walked the answers. Here's what that looks like for a Longhorn donor search.
A donor researcher needs wealth, alumni ties, and board seats. Instead of us pre-wiring which tables hold those, the report queried the graph's catalog in plain language and got back the exact edges to walk — with the rules for using each one:
Every one of those edges arrived with its ranking signal and its integrity rules attached. As the graph adds new data — say a customer/supplier edge next month — this same report finds and uses it automatically, with no rewrite. That's the point of a self-describing graph.
Walking University of Texas at Austin → studied_at returns 1,750 alumni; filtered to leaders of
$1B+ firms, 96. But the graph's real power for UT is the capacity edge — alumni who are public-company
insiders. Ranked by disclosed insider value:
| Alum | Company (insider) | Disclosed value |
|---|---|---|
| Rex W. Tillerson | Exxon Mobil Corporation | $17M |
| Thomas O. Hicks | Clear Channel · Cimarex · Carpenter Tech | $8M |
| Kriss Cloninger III | Aflac | $7M |
| Julie J. Robertson | Noble Corp | $3M |
| Mark E. Monroe | Continental Resources | $2M |
A Texas oil-and-energy who's-who — exactly the affinity you'd expect at UT, surfaced by the graph, not a hand-built list. (Sampled 90 alumni; 44 carried a capacity edge.)
The killer move: the graph found boards where two UT alumni serve together — an instant warm introduction between prospects (or from one you already know to one you don't):
This is a 2-hop traversal (alum → board → co-directors) filtered
to fellow Longhorns. Cultivating one warms the path to the other — the difference between a prospect list
and a strategy.
Nothing in this report was hardcoded to UT or to donor research. It discovered the relevant edges from the graph's catalog, walked them with the correct ranking + integrity rules attached, and assembled capacity + affinity + the warm path. Point it at any school, any prospect type — and as the org keeps feeding the graph, the same report gets deeper on its own, with no rewrite. That's the compounding advantage: every table the company adds makes every report smarter, automatically.