Competitor price monitoring that flags the shelf before the customer notices.
The wholesaler baseline said your prices were right. Then someone finally measured the basket against the big box down the road — and the keys, the batteries, the tape were priced over it. We build AI that reads competitor shelf prices against your price file every week and alerts your merchants before your regulars do.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
No competitor price analysis — just blind hope the baseline is right.
From pricing desks we have sat inside: a hardware retail co-op member running twenty-odd stores and a couple hundred thousand item records, where the wholesaler sets the base retails and nobody checks them against the market.
Blind hope as a pricing strategy
The honest version, said out loud on the call: we don’t house a database for competitive pricing — it is blind hope that the co-op is doing their job well. The wholesaler’s baseline becomes the price file by default, because nobody has the market’s numbers.
1.15 trusted, 1.19 discovered
The plan was to ride the wholesaler baseline at 1.15 times the index. When someone finally measured the full basket, it sat at 1.19 — four points of drift nobody saw, because nothing was watching.
The keys a regular can price cold
Every store carries the two or three hundred items customers already know the price of — the keys, the tape, the cleaner. Be over the big box on those and you are outpriced out of the market one basket at a time, driving footsteps out of the business.
The customer is the alert system
The worst version of the week: a regular mentions the big box sells it cheaper, and that is how the pricing desk finds out. The market moved — your data just wasn’t in the room.
A price file with no outside view
Two hundred thousand item records priced against costs, hot sheets, and a baseline — and not one of them priced against the store down the road. After a year of taking price, there is nothing left to push against: you can’t take price year over year forever.
Nobody knows what a move did
You raised the price and the volume moved — but nobody can say by how much, because the competitive context was never captured. Raised prices X, killed the volume by Y, and nothing pushes that to the merchant.
The basket drifts four points while nobody watches.
What the blind spot looks like inside a twenty-store retail group pricing off a wholesaler baseline. Your numbers will differ — the first competitive read puts figures on yours before anything gets built.
From an anonymized engagement — a hardware retail co-op member with twenty-odd stores, $100M+ revenue, and a wholesaler-set base retail
Competitor price monitoring run against your price file, every week.
No new system for your merchants to learn. The comparison your best merchant does in the parking lot becomes the system — AI reads the market overnight, and the pricing desk starts the week with only what needs a decision.
- 01
Build the competitive database you don’t house
The big-box stores and local competitors that matter, the categories that matter, the known-value items first — a watchlist that lives in a database you control, not inside a vendor’s seat license.
- 02
Encode your floors and your gaps
Margin floor by category, how far over the big box is too far on a known-value item, where a premium holds because you have it and they don’t — the judgment your merchants carry becomes written rules, yours to keep.
- 03
AI reads the shelf every week
Agents collect competitor shelf prices — the price scraping is the easy part — match them to your item master, run the gaps against your floors, and flag what you are over on with the explanation attached. A merchant approves before anything moves: trust, but verify first.
Price monitoring software scrapes. It doesn’t know your margin floor.
Price monitoring software and competitor price tracking software sell seats on a scrape: enterprise price, enterprise implementation, a dashboard somebody has to open. The tool can tell you the big box moved a price. It cannot tell you that the item is one of your three hundred known-value items, that the gap breaks your margin floor, or that a 1.19 basket is four points off the baseline you trusted.
We are an AI operating partner — we build and run the machine. Competitive price tracking runs against your price file, on your rules, in your cloud, and lands as a short exception list with the why attached. Your merchants keep their judgment; they just stop being the alert system.
We don’t house a database for competitive pricing. It is blind hope that the co-op is doing their job well.
Led by Peter Enestrom.
Asked by merchants and pricing leads.
The straight answers, before you book anything.
Competitor price monitoring is systematically watching what the market charges for what you sell — the big box down the road, the regional chain, the online shelf — and comparing it against your own price file every week. For a retail merchant, the output is not a dashboard; it is a short list of items where your price is out of position, flagged before customers notice. Competitive price tracking at store scale is an AI job: thousands of items across a handful of competitors is more reading than any pricing desk has hours for.
Price monitoring software sells you the scrape — seats, dashboards, a feed of competitor prices. Useful raw material, but the software does not know your margin floor, your known-value items, or which gaps actually cost you footsteps. We build the monitoring on your rules instead: AI reads the market, runs it against your floors and your price file, and your merchants review only the exceptions. The tool collects; the system decides what needs a person.
Yes — agents read publicly listed shelf prices from competitor sites and weekly ads, respectfully and on a schedule, and match them to your item master by the product codes every retailer shares. Price scraping is genuinely the easy part; the hard part is knowing that the drill you are $6 over on is one of the three hundred items your regulars can price cold. That judgment is what gets encoded.
Four things, every week: which items you are over on, by how much, against whom, and whether it matters. A battery SKU two dollars over the big box is a price-image problem; a specialty part two dollars over is a premium that holds. Good competitor price analysis separates the two against your margin floors, so the pricing desk acts on a page of exceptions instead of a wall of data.
Because the baseline is set for thousands of members across every market — not for your stores against your big box. One co-op member trusted the baseline at 1.15 times the index and discovered the full basket had drifted to 1.19, with the known-value items priced over the store down the road. Trust, but verify first: the baseline stays your starting point, and the weekly market read tells you where it stopped being right.
No — blanket matching is how independents go broke. The point of competitor price monitoring is knowing, not copying. Your encoded rules decide where to compete: close the gap on the known-value items that shape price image, hold the premium where you stock what they don’t, and never break a margin floor to win a basket that was never price-shopped.
Same pattern, different buyer. Our competitor monitoring page serves professional-services firms — AI reads SEC filings, news, and executive moves across a client watchlist. This page serves retail merchants: AI reads competitor shelf prices against your price file and margin floors. Both run on the same build — your watchlist, your rules encoded, people getting only what needs them — but the data, the rules, and the buyer are different.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.