Find the position shapes worth backing at your ticket size.
Tell us your lens. Ticket size, hold period, sector preference. We surface the thesis-zone operators on the public record at your scale, the anti-patterns to avoid, and the named operators whose position is worth modeling.
Two minutesThree picksA thesis
Starts with ticket size
Companion to Part Seven of the series · Who feeds the operator?
1 of 3 · Ticket size
What ticket size do you write?Per-deal check size on a typical investment
Pick the band that fits your committee's bar.
Selected. Scrolling to hold…
2 of 3 · Hold period
What hold are you underwriting?Years to exit or distribution
Pick the horizon the LP committee will judge you on.
Selected. Scrolling to sector…
3 of 3 · Sector preference · pick one or more
Where does your thesis sit?The operating segments you'd back
Multiple selections allowed. The thesis adjusts to span the segments you back.
Add more above if your thesis spans multiple sectors.
Your thesis
Back operators who hold all three faces at your ticket size.
The thesis-zone operators at your ticket size are the proven winners. The anti-patterns failed because they engineered one face and stayed exposed on the others.
The position landscape at your lens.
Fifty-two operators on the public record. Operators in your sector lens are highlighted; the rest dim.
Thesis-zone operators
Anti-pattern operators
Methodology
The 52-operator dataset spans six segments. Thesis-zone classification uses public outcome evidence, filings, continued operation, segment leadership at multiple. Anti-pattern classification uses documented closure, Chapter 11, or major restructuring. Sector filter maps to segment (fast-casual chains = national-chain and regional-chain; multi-unit independents = restaurant-group; heritage = legacy; open = all). Distance matching uses position shape against a target derived from ticket size and hold period.