The building, the owner, the trade, the street, the crowd — cross-referenced across the public record. Site DNA reads what makes your existing sites work, then finds the handful of premises in London that match — including the ones that aren't on the market yet.
All open public data — assembled, cleaned and joined onto a single premises key. The join is the hard part; no two of these share an identifier.
It reads your existing sites — size, heritage, pitch, catchment, footfall — and turns what they have in common into a measurable DNA.
Every venue is ranked against that DNA, live. Raise the bar and the shortlist emerges — explainable, dimension by dimension.
Signs of pressure — sitting empty, insolvency,
financial stress, ageing ownership.
Market motion —
recently marketed, change of use. Then the owner research, and the approach.
The world model is neutral — only the DNA vector changes.
Three Grade II-listed wine restaurants. The engine reads their DNA — heritage, ~250 m², central characterful streets — and surfaces the ~120 London venues that share it, then narrows to what might be gettable.
View on the map →Thirteen neighbourhood kitchens. A completely different DNA — small high-street units, residential density — plus their delivery reach, showing the white space where the next kitchen goes.
View on the map →