Case study part 1 · real public data · part 2: the operating graph, live →

The dependency graph Red Lobster didn't have.

In May 2024, America's biggest seafood chain filed Chapter 11 in Orlando with about 100,000 creditors. The autopsy — bankruptcy filings, court records, and a 2026 creditor lawsuit — reads like a graph query: an owner who was also the sole supplier and a creditor, and a landlord created by the chain's own leveraged buyout. Below is that story as an entity graph, built entirely from public sources. Click any circle to drill in.

● companies ● people ● places ● orgs ● events/promos — blue link = sole source

What the graph knew that the org chart didn't

None of this was hidden. Every fact in the graph above was public — SEC-adjacent filings, court records, trade press — before the collapse. What was missing was the join:

One entity, three roles. Thai Union was simultaneously Red Lobster's controlling owner, its shrimp supplier, and one of its creditors. In 2023, after a "quality review" that the 2026 creditor lawsuit calls pretextual, competing shrimp suppliers were dropped — leaving the owner as sole source for roughly half of all shrimp types, at prices filings describe as above market. Decisions steered that way cost the chain an estimated $76 million. A dependency graph flags an owner-supplier-creditor triangle automatically; a spreadsheet of vendors never will.

The landlord the company built for itself. Golden Gate Capital's 2014 buyout was financed by selling the land under ~500 restaurants for $1.5 billion — cash that went to the deal, while the leases (many above market) stayed with the restaurants. Final-year rent: over $190 million. In the graph, that's a single load-bearing edge pointing at a REIT.

The promo was a symptom, not the cause. $20 Ultimate Endless Shrimp lost $11 million — the number everyone laughed at. The structural dependencies above it are what actually broke, and they were visible years earlier to anyone — or any AI — that could query the graph.

Red Lobster's new CEO says he'll build the most AI-forward restaurant company in America. Below the chatbot layer, this is what AI-forward means: your suppliers, landlords, owners, and contracts as a scored graph your team — and your AI — can query before the risk becomes a filing. I build these in weeks, from your data and public records. The lab takes two builds a quarter.

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