Price Mirror
Log the price you were just shown next to a clean second look at the same item, and find out if you got a personalized rate.
PriceMirror
Log the price you were just shown next to a clean second look at the same item, and find out if you got a personalized rate.
Date: 2026-10-01 Form factor: web app Status: Prototype
What it is
PriceMirror is a personal ledger for catching surveillance pricing: the practice of a retailer, rideshare app, or delivery service charging you a different amount than the next shopper sees for the identical item, based on your device, login history, location, or purchase pattern. You log the price you were quoted alongside a "clean" reference price — pulled from a logged-out browser, a different account, or a friend's phone — and PriceMirror calculates the gap, flags anything outside normal price noise, and drafts a disclosure request you can send to the merchant.
Who it serves
Shoppers who already suspect they're paying more than someone else for the same cart: grocery-delivery users comparing notes with a partner's account, rideshare riders who've seen a friend get a cheaper fare for the identical trip, and anyone shopping while logged into a loyalty account who wants to know if that login is quietly working against them.
Why it could be profitable
Free tier: manual price-check logging, variance calculation, and the disclosure-request draft. Paid tier ($5–7/month): saved comparison templates per merchant, a running "total suspected overcharge" tally across checks, and pre-filled complaint letters routed to a state attorney general or the FTC's complaint portal instead of just the merchant.
The timing is real. On August 19, 2026, the FTC published a proposed enforcement policy stating that failing to disclose personalized pricing can violate the FTC Act's ban on unfair or deceptive practices — the first time the agency has drawn that line explicitly (Grocery Dive). Consumer Reports' own testing found Instacart prices for identical items varying by up to 23% between shoppers, and a separate analysis put the gap between the cheapest and priciest quoted rideshare fare for the same trip at roughly 42% (Fortune). What's thin: the FTC policy is a comment-period proposal, not a final rule, and nobody has built the self-serve documentation tool a consumer would need to act on it yet — which is the opportunity, but also means there's no proof people will pay for it before enforcement actually bites.
Form factor & scope
A single-page web dashboard. This prototype demonstrates the full loop for one user: a ledger of past price checks, a form to log a new one, automatic flagging against a variance threshold, and a generated disclosure-request letter — all running on sample data, with no account system or live price-fetching.
How to run it
- Open
index.htmlin any modern browser. - The ledger loads from
sample-data.json(bundled as an inline JSON block so the page works straight fromfile://, no local server needed). - Click any flagged row to see the variance breakdown and generate the disclosure-request letter. Use the "Log a new check" form to add your own comparison and watch it get scored live.
What's in this prototype
- A ledger view of 14 sample price checks across grocery delivery, rideshare, online retail, and travel booking, each showing the quoted price, the reference price, and the computed variance
- Automatic flagging of any check with a variance at or above a 10% threshold, with a running count of flagged checks and an estimated total overcharge
- A detail panel per flagged check with a side-by-side price bar and category benchmark context (the Instacart and rideshare figures above)
- A working "log a new check" form that scores and inserts a new entry into the ledger without a page reload
- A disclosure-request letter generator that fills in the specific merchant, item, prices, and date, citing the FTC's August 2026 policy language
Roadmap
- Browser-based screenshot capture so a price check doesn't require manually copying numbers
- A shared, anonymized benchmark database so a single user's checks compare against thousands of others in the same category
- Scheduled re-checks for recurring purchases (weekly grocery order, regular commute route)
- Direct filing integration with state AG consumer-complaint portals
- A merchant-facing version that lets retailers self-certify disclosure compliance
Sources
- FTC moves to make retailers disclose use of 'personalized pricing' as technology now enables broad consumer surveillance (Fortune, Aug 24, 2026) — FTC Chairman Ferguson's statement, the Section 5 disclosure theory, and the Instacart/rideshare variance figures
- The FTC is turning its sights on personalized pricing. What does it mean for customers? (Grocery Dive, Aug 2026) — details on the August 19, 2026 proposed enforcement policy and what data retailers use to set individual prices
Requirements
PriceMirror — Requirements
Goals
- Let a user log two prices for the same item (the price they were shown, and a reference price) and see the variance instantly.
- Surface checks likely to reflect personalized pricing without requiring the user to know a specific threshold.
- Produce a ready-to-send disclosure request the moment a check is flagged.
- Keep the whole flow usable with zero account setup and zero network calls beyond loading bundled sample data.
Primary user
A shopper who already has a reason to suspect personalized pricing — they compared notes with a friend, used a second device, or read about the FTC's August 2026 policy and want to check their own receipts. They are not a data analyst; the tool needs to do the variance math and the flagging for them, not hand them a spreadsheet.
Functional requirements
- FR1: The app loads a ledger of price checks from sample data on page load, with no server required.
- FR2: Each ledger entry shows category, merchant, item, quoted price, reference price, computed variance percentage, and a flagged/not-flagged state.
- FR3: Variance is computed as
(quotedPrice - referencePrice) / referencePrice * 100. - FR4: Any entry with an absolute variance at or above 10% is automatically marked as flagged.
- FR5: The user can filter the ledger by category and by flagged-only status.
- FR6: The user can add a new price check via a form (category, merchant, item, quoted price, reference price, context notes) and see it scored and inserted into the ledger without a page reload.
- FR7: Clicking a flagged entry opens a detail view with a visual price comparison and the category benchmark (Instacart grocery-delivery variance, rideshare fare variance) for context.
- FR8: The detail view includes a "Generate disclosure request" action that produces letter text pre-filled with the merchant name, item, both prices, the variance, and the check date.
- FR9: The generated letter cites the FTC's Section 5 disclosure theory from its August 19, 2026 proposed enforcement policy in plain language.
- FR10: The user can copy the generated letter text to the clipboard with one click.
- FR11: Summary stat tiles show total checks logged, total flagged, and an estimated total overcharge across flagged checks.
- FR12: Form inputs validate that both price fields are positive numbers before allowing submission.
- FR13: The ledger persists only for the current page session; no data is written to disk or a remote server.
User stories
- As a shopper, I want to log the price I was just quoted next to a reference price, so that I can see the gap without doing the math myself.
- As a shopper, I want checks above a meaningful variance to be flagged automatically, so that I don't have to guess what counts as suspicious.
- As a shopper, I want to see how my variance compares to known cases like Instacart's or a rideshare app's, so that I can judge whether my situation is ordinary or not.
- As a shopper, I want a disclosure-request letter already filled in with my specifics, so that I don't have to write one from scratch.
- As a shopper, I want to filter my ledger by category, so that I can review just my grocery-delivery checks or just my rideshare checks.
- As a shopper, I want a running total of my flagged checks and estimated overcharge, so that I have a number to point to if I escalate a complaint.
Non-functional requirements
- Works fully offline after the initial page load — no analytics, trackers, or third-party scripts.
- No personally identifying information is required to use the demo; sample data uses generic merchant and item names.
- Readable on a phone-width viewport as well as desktop, since a user is likely to log a price check right after seeing it on their phone.
- All computation happens client-side; nothing in this prototype transmits data anywhere.
Out of scope (for the prototype)
- Automated price capture via screenshot, browser extension, or API integration with any retailer.
- A real database, user accounts, or login.
- Actually submitting the generated letter to a merchant, state AG, or the FTC.
- A crowdsourced or shared benchmark dataset across users.
Open questions
- Should the variance threshold be adjustable per category, since a 10% gap might be normal for airfare but unusual for a fixed-price grocery item?
- Does a disclosure request carry any legal weight before the FTC's proposed policy is finalized, or should the letter language be framed as a question rather than a demand?
- Would users trust a self-reported reference price enough to act on the flag, or does this only become credible once there's a shared benchmark dataset?
