We Asked 4 AI Assistants Which Software Runs a Specialty Store

A growing share of store owners no longer start a software search on Google. They open ChatGPT and ask. So we ran an experiment: on a single day in August 2026, we put the same 20 real buyer questions to ChatGPT, Gemini, Claude, and Perplexity, using one identical standardized instruction for every request, and scored all 80 answers with the same rules. The full results, the exact instruction and question list, and every limitation are in our new research report, and three findings stand out.

1. The wording of your question decides which software exists

Ask a generic question like "what is the best system for an independent retailer" and every engine hands you the same big general-purpose names: Square, Shopify, Lightspeed. Across all 20 generic retail and jewelry answers we collected, not one specialty vendor was mentioned. Not one.

Name your store type, though, and the answers change completely. Every pawn-specific question we asked, on every engine, surfaced software built for pawn operations, with loan tickets, layaways, and compliance reporting in the feature discussion. Bravo appeared in 16 of 16 pawn answers.

2. The engines do not agree with each other

The same question produced different shortlists on different engines, on the same day. In our panel, Perplexity named Bravo in 14 of 20 answers, ChatGPT and Claude in 10 of 20, and Gemini in 9 of 20. A buyer who consults one assistant is sampling a single opinion from a panel that visibly disagrees with itself.

Practical takeaway: treat an AI shortlist the way you would treat one friend's recommendation. Ask a second engine, then verify the feature claims on the vendor's own site, because the engines also disagreed about details like payment processors and pricing.

3. Specialty software wins where it matters

Overall, Bravo was named in 43 of the 80 answers and ranked first in 30 of them, driven almost entirely by questions that named a store type or asked for a comparison. The general retail platforms dominated raw mentions because they own every generic question. The pattern is consistent: category depth is what the engines reward once the category is visible in the prompt.

Read the full report

The complete study, with per-category tables, per-engine results, the vendors most mentioned across all answers, the exact standardized instruction and question list, and every limitation we know about, is published here:

The 2026 AI Search Visibility Report: Specialty Retail Software

We built it the way we would want research done to us: the full prompt set and the standardized instruction are disclosed word for word, the parsing rules treat every vendor identically, and the categories where Bravo was rarely or never named are reported alongside the ones where it swept. We will re-run the panel and publish the next snapshot as the engines evolve.