Wilson Wu
Chief Revenue Officer, Snappy
Most operators shopping for a POS, an online ordering platform or a loyalty program in 2026 open an AI chatbot before they open Google. A March 2026 survey of 1,076 B2B decision-makers found that 51% now start software research with an AI chatbot more often than with a search engine, up from 29% in April 2025. Used well, that compresses two weeks of tab-hopping into an afternoon.
Used badly, it hands you stale pricing, vendors that do not operate in your country, and a confident answer with nothing behind it. This is the operator’s version of that skill: how to prompt, what to verify, and why some genuinely good vendors never show up at all.
Summary
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Operators start inside an AI chatbot and check the answer on Google after: G2’s research, based on 1,076 B2B decision-makers across North America, EMEA and APAC, found 93% say AI chatbots fundamentally changed how they research software, while 80% still use Google somewhere in the journey.
The two are not substitutes. The chatbot builds the list; the search engine and the vendor site confirm it.
Separate research puts ChatGPT at 72% usage among B2B buyers evaluating vendors, with 44% also using Perplexity during shortlisting, citing Forrester’s 2026 B2B Buyer Journey report, plus Bain research putting the average buyer at 17 AI search queries per week.
What operators ask is predictable too. G2 categorised opening prompts: 33% start with a category question, 31% with a competitor question, 22% with requirements, 9% with ecosystem fit and 6% with budget. Most people open with “best POS for a small restaurant” or “alternatives to the system I have now.” Both are the weakest prompts available.
AI assistants are reliably good at three jobs, explaining a category, drafting demo questions and comparing two products in plain language, and reliably bad at one: current pricing and current country availability, which change fastest and get quoted from stale pages most often.
Good at: explaining a category you have never bought before, generating the questions you should ask on a demo call, and summarising the difference between two products in plain language, such as when you ask which POS system fits your restaurant. G2 found these are the top-ranked uses in practice, led by comparing vendor strengths and weaknesses.
Bad at: current pricing and current country availability. Those two change most often and are the ones most likely to be quoted from a page written eighteen months ago. 64% of buyers report hitting inaccurate recommendations often or very often, so this is not an edge case.
The assistants also draw on different places, which is why they disagree.
| Assistant | Where its sources skew | Citation behaviour | Best use in a tech search |
|---|---|---|---|
| ChatGPT | Reference and encyclopedic sources. Wikipedia alone accounts for 47.9% of its top-10 source share | Mentions brands roughly 3.2 times more often than it cites them; brand citation rate measured at 0.59% | Category education and drafting your demo question list |
| Perplexity | Community and review aggregators. Reddit is 20% to 24% of all its citations, and it favours G2, Gartner, TripAdvisor and Yelp (State of AI Citations 2026) | 8.2 sources per answer, roughly 3.4x ChatGPT | Finding what actual operators say, and chasing the linked threads yourself |
| Google AI Overviews / AI Mode | Google's own properties, which take roughly 43% of citations | Across AI tools generally, only about 12% of AI-cited URLs overlap Google's top-10 organic results | Fast factual checks, not shortlist construction |
The practical consequence sits in one number: only 11% of domains are cited by both ChatGPT and Perplexity. Ask both. Where they agree, you have a real signal. Where they diverge, you have found the part of the market that is genuinely contested and worth your own reading.
A constraint prompt is a prompt built from your restaurant’s own facts, format, seats, weekly volume, province and required modules, instead of a generic category question. Give the assistant those facts and it stops recommending vendors that cannot actually serve you.
A prompt like “best POS system for restaurants” only returns a listicle summary, because your restaurant is not a listicle. Replace it with a constraint prompt like this one:
I run a 60-seat full-service restaurant in Montréal with one location, about 900 orders a week, roughly 35% off-premise. I need POS, kitchen display, online ordering and loyalty in one system. I need Revenu Québec WEB-SRM compliance and a French guest-facing interface. Which vendors meet all of that, and which of my requirements does each one fail? Cite a source for each claim.
Four things make that prompt work.
Name your country and province. Availability is the most common error in AI answers about restaurant technology, and the assistant will recommend a US-only platform unless told not to.
Name the modules together. Asking about a POS gets POS answers. Asking about POS plus ordering plus loyalty forces the assistant to reason about what is bundled and what is a separate line on your bill.
Ask what each vendor fails. Assistants default to advocacy. Requiring a failure list per vendor converts a sales summary into a comparison.
Ask for a source per claim. The highest-leverage instruction in the prompt. It separates claims with a citation behind them from the model’s own generalisations, and it hands you a list of pages to check.
Then follow up with the question nobody asks: “What did you not consider, and what would change your answer?” That one regularly surfaces regional vendors the first pass dropped.
Your AI assistant built the shortlist. Pressure test it.
Send the vendors it named, along with your seats, weekly volume and province, and we will go through the list with you.
Show me my shortlist gapsFour claims, every time, before the demo call rather than after: country and province availability, published price versus quoted price, what is included versus a paid add-on, and the processing rate together with the contract term, each one easy to miss until the quote arrives.
1. Country and province availability. A vendor’s funding announcement and its pricing page footer can say different things about which countries it serves, and both will sit on the same website. Ask for written confirmation of availability, billing currency and local install coverage. In Québec, add written confirmation of Revenu Québec WEB-SRM compliance, because a non-compliant register is disqualifying regardless of how good the rest of the product is.
2. Published price versus quoted price. Many restaurant platforms publish no dollar figure at all. When an assistant quotes a monthly number, check whether it came from the vendor’s live pricing page or a third-party review article of unknown vintage.
3. Included versus add-on. This is where quoted comparisons fall apart. Online ordering, loyalty, kitchen display and reporting are core in some platforms and paid add-ons in others. Ask for a quote covering every module you will actually switch on, not the base plan.
4. The processing rate and the contract term. The effective card-processing rate on your real card mix usually outweighs the software fee, and terms with early-termination fees are normal in this category. Neither is something an assistant can know about your business.
Here is how those four checks split between what a chatbot can tell you, where the real answer lives, and how we answer them ourselves.
| What to verify | What an assistant can usually tell you | Where the answer actually lives | How Snappy answers it |
|---|---|---|---|
| Country and province availability | Which countries a vendor markets in, often lifted from an old press release | The pricing page footer, the billing currency and a written confirmation | Offices in Toronto, Vancouver and Montréal; WEB-SRM compliant, and we will confirm it in writing, the same as you should ask of anyone else |
| Published price versus quoted price | A monthly number from a third-party article of unknown vintage | The vendor's live pricing page, or a written quote when none is published | No public list price; ask Snappy for a written quote covering the modules you name |
| Included versus add-on | Which modules exist, rarely which ones are bundled | The quote itself, line by line | Point of sale, kitchen display, kiosk, dine-in QR, online ordering, reservations, waitlist and loyalty on one system |
| Processing rate and contract term | Nothing about your card mix or your termination clause | The processing agreement and the contract, in full | Snappy Pay is quoted alongside the software; ask for the effective rate on your own card mix and the term in the same email |
A useful habit: paste the assistant’s claim back to the vendor verbatim and ask them to confirm or correct it in writing. One email turns a generated sentence into an accountable one.
Because they are structurally invisible, not because they are bad: 51% of B2B tech brands have zero citations across ChatGPT, Perplexity and Gemini, half the category absent from the tool half the market now starts with. The causes are mechanical.
Language models extract from structure. The original generative engine optimization research from KDD 2024 found that the framework, whose highest-performing tactics include adding statistics, citations and credible references, boosted visibility in generative engine responses by up to 40%, with efficacy varying by domain. Markup pushes the same way: a FAQ tagged as question and answer is easier for a machine to lift than the same content buried in a paragraph, which is why it has become standard on any vendor page that wants to be cited.
So a vendor with a beautiful site, no published pricing, no comparison tables and no FAQ markup is close to unreadable to an assistant, even when it is the right answer for your restaurant. Two implications for a buyer:
We will state the obvious about our own position, since this article makes the argument publicly: it is the same reason vendors, Snappy included, now write comparison tables, sourced statistics and real FAQ sections. Content a machine can extract is also content that answers your question without a sales call. For the diner-facing version of the same problem, see how to make your restaurant visible inside ChatGPT and Google AI Mode, and for the wider picture, the 8 AI trends Canadian restaurants should adapt in 2026.
Five steps, ninety minutes, run this week: write one constraint paragraph, run it through two assistants, verify the four facts on each vendor’s own site, call three real operators, and send one identical quote request to everyone, so every quote covers the same scope.
Step 1: Write the constraint paragraph once (15 minutes). Format, seats, locations, weekly order volume, off-premise share, province, required modules, compliance needs, and what you are replacing. Save it. You will paste it into every tool and every demo email for a month.
Step 2: Run it through two assistants (20 minutes). ChatGPT and Perplexity, same paragraph, both asked for a source per claim and a failure list per vendor. Keep every vendor either one names. Overlap is your high-confidence list; the difference is your research list.
Step 3: Verify the four facts on each vendor’s own site (30 minutes). Availability, published price, included versus add-on, contract term. Anything you cannot find on the vendor’s own page becomes a demo question rather than a fact.
Step 4: Find three operators, not three reviews (15 minutes). 99% of Perplexity’s Reddit citations point to specific threads rather than subreddit homepages, so open them. Then call someone in your own city who runs your format. One phone call outweighs a page of star ratings.
Step 5: Demand a like-for-like quote (10 minutes to send). One email per vendor: every module you will switch on, hardware, install, the effective processing rate on your actual card mix, contract term and early-termination fee. Same email to everyone. The comparison only means something when the scope is identical.
Where this lands for us: Snappy runs point of sale, kitchen display, kiosk, dine-in QR, commission-free online ordering with Google integration, reservations, waitlist and loyalty on one system, with offices in Toronto, Vancouver and Montréal and support from 10am to 10pm ET, seven days a week. Put us through step 5 alongside everyone else. That is the point of step 5.
It can build a credible shortlist, and it will change your mind more often than you expect. G2 found 69% of B2B buyers chose a different vendor than originally planned and 33% bought from a vendor they had never heard of. It cannot reliably tell you current pricing, current country availability or your processing rate. Use it to build the list and write your demo questions, then verify every number on the vendor’s own page.
Use both, because they read different parts of the internet. Only 11% of domains are cited by both. ChatGPT leans on reference sources and explains a category better, while Perplexity cites roughly 8.2 sources per answer and draws 20% to 24% of its citations from Reddit, which surfaces what operators actually say. Where both name the same vendor, you have a real signal.
Treat every quoted price as unverified. 64% of B2B buyers encounter inaccurate AI recommendations often or very often, and pricing, add-on packaging and country availability all change faster than the content models are trained on. Open the vendor’s live pricing page, and if no price is published, ask for a written quote covering every module you will turn on.
No. 51% of B2B tech brands have zero citations across ChatGPT, Perplexity and Gemini, which usually reflects unstructured web content rather than product quality. Regional vendors are systematically underrepresented. Ask the assistant which vendors it has low information about, then check trade press and local operator communities before ruling anyone out.
How restaurant operators choose restaurant technology has already changed, and it changes outcomes: nearly seven buyers in ten end up somewhere other than where they started. That is a good thing when the assistant surfaces a vendor you would never have found, and an expensive one when it quotes a price that expired last year.
The discipline is simple. Prompt with constraints instead of categories, then run the same question through two assistants and read the disagreement.
Verify availability, price, packaging and contract term on the vendor’s own page, then talk to a real operator in your own city.
Then send one identical scope to everyone and compare what comes back.
With 41% of Canadian foodservice businesses at or below breakeven, a bad software decision is no longer absorbed quietly. AI makes the shortlist faster. It does not make the decision.
Want a like-for-like quote you can put beside whatever your assistant recommended, with the processing rate on the table from the first conversation? Talk to the Snappy team.
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Book a walkthrough, name every module you would switch on, and ask for availability, packaging and contract terms in writing, the same as you would ask any vendor.
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