Exact resolution
Brand, category, leaf-category, and state mentions are resolved against the live catalog dictionary, not guessed.
Pillar two · Platform
AI / NLQ-Enabled Solutions
"Urgent care in Georgia and Florida." "Every Jersey Mike's within the Atlanta MSA." Type the question; get the exact cut, its row count, and its price.
How the natural-language layer works
Honest ambiguity
When a term matches more than one thing, the explorer surfaces the possible matches for you to choose.
Reproducible output
Every result is a deterministic cut of a named release, with counts, a sample, and a formula price.
Grounded in the data
The model proposes; the catalog decides. A verified catalog fact always beats a model guess.
Where you can use it
The store explorer, for building and buying custom cuts.
Embedded in your own product under an embedded license.
Analytics engagements, where the same layer drives ad-hoc questions against your results.