Happy Cabbage is making AI ordering and menu planning more accurate for New Jersey retailers.

Would you bet a million dollars on AI’s accuracy? For retailers using AI to order and menu plan, running a handful of $30,000 orders a month, that’s roughly what’s at stake over the course of a year. That’s not a reason to avoid AI. It’s a reason to be specific about what you’re trusting it to do.

AI can be useful for cannabis ordering and menu planning: it drafts orders. More importantly, it matches messy data across wholesale sheets and platforms. It’s also easy to trust past the point it’s earned that trust.

AI is Confidently Wrong and it Will Cost You

“If you’re about to spend $30,000 on an order through an AI, you want to make sure the math behind it is correct,” said Andrew Watson, CEO of Happy Cabbage.

That’s harder than it sounds for retailers pulling straight from a Point Of Sale (POS) system or a raw export. AI doesn’t have a way to check its own work.

“It doesn’t have the ability to know whether or not anything it’s produced is actually informational or not. So, it can just guess,” explains Danny Gold, COO of Happy Cabbage.

Point it directly at a POS export or a full sales history, thousands of Stock Keeping Units (SKUs) deep, and it doesn’t just risk a wrong answer. It burns through credits doing it.

There are a couple of scenarios where AI can’t account for with POS data or exports.

For example, in rotating SKUs, say a store carries a product with twelve rotating variants, with only two or three in stock at once. Ask an AI which single trackable version of a product SKU to reorder based on the last 30 days of sales. It has no way to know which of the twelve were on the shelf during that window. So, it recommends restocking all twelve at full volume.

“You’ll get an order that is three times the size of what you need, easily,” Watson said.

That’s because the store’s customers were never buying twelve separate things. They were substituting between whichever two or three happened to be available, and a SKU-by-SKU view of the data can’t see that substitution happening.

Markdowns are another issue AI can’t account for.

“Everybody has the story, and I just heard it,” said Gold. “We’d just gotten off a

call with a customer who said: I’m using these VMI reports from brands. They keep telling me that I’ve got this fast mover that I need to restock. And it was a fast mover because I discounted it 50% to clear it out.”

A markdown and a trend produce the same shape in a sales report. So, nothing in the raw numbers tells a buyer, or an AI reading them, which one it’s looking at.

Another thing to watch out for is that keeping all this straight requires a lot of context together. That usually means one long chat. The longer that chat runs, the less reliable it gets.

“Any time the context is compacted, your percentage of hallucinations increases. Because what it is doing is summarizing the data itself and then only reloading that for the rest of the work,” Gold explained.

The more a retailer leans on AI alone to untangle its own data, the more it costs in credits and the less it can be trusted.

Keep the Math in a System You Can Trust

The fix isn’t asking an AI to be more careful. It’s not letting it do the math in the first place. In Happy Buyers, run rates, days on hand, and demand are calculated by the platform, not reinvented by an AI.

“All this data is cached every night. Which means if you were to pull it into an AI tool or chat with an agent around it, it can just use all of that data that we rolled up accurately at every intersection and not try to calculate it,” Watson explained.

Once the math is handled somewhere trustworthy, AI is free to draft, match, and explain instead of guessing.

Using Happy Buyers, retailers trust AI to draft purchase orders for a single brand or a whole product line, using numbers they never have to second-guess. Since the math is backed by Happy Buyers, it also flags orders that could leave a store overstocked. The same trust extends to matching a wholesale menu against a store’s own catalog.

“Where the LLMs (Large Language Models of AI) really come in is product matching, fuzzy matching,” Gold says. “That’s really where leveraging an LLM is going to pay dividends.”

Retailers use AI, Happy Buyers, and market data to study their hyper-local market and decide what to cut from the menu, and where there’s room to add a local best seller.

Happy Cabbage Is Trusted by New Jersey Retailers to Keep Their Math Accurate

Retailers across New Jersey are already using Happy Buyers to keep that math straight, connecting the AI tool they already use to draft orders, audit inventory, and plan menus on verified numbers.

Learn more at Happycabbage.io/ai-hub.

Curious retailers can start with a 14-day free trial.

(Full disclosure this is an advertorial part of an advertising deal between Happy Cabbage and Heady NJ).