CartLens Pitch Deck — Local Prices from Real Receipts

CartLens

01 / Investor overview · October 21, 2026

Scan receipts. Compare prices shared by nearby shoppers.

Real receipts. Local prices.

CartLens turns shopper-contributed receipts and shelf tags into a shared price map. See what others paid and find lower prices for your next trip.

Investor overview · Planning case

Retail Price Opacity

02 / The problem

Shoppers have no quick way to know if a price is fair, before they buy or after. Item-level prices vary by store, while comparing a whole receipt across nearby shops remains difficult.

Hidden prices

Store prices are hard to compare across a full local basket.

Wasted money

Choosing a higher-priced local store can add up over time.

Too hard to track

Comparing prices by hand takes too much effort.

Locked data

Spending details stay on paper receipts or in closed bank apps.

Privacy cost

Many discount programs require shoppers to share purchase histories.

Three Core Shopper Personas

03 / Target segments

Target personas to test in the pilot.

The budget shopper

Shops on a tight budget and wants to know which nearby store charges less for everyday items.

The privacy-first shopper

Wants useful price comparisons and control over which receipt details are shared.

The busy shopper

Wants to scan a receipt and find nearby price options without checking every store by hand.

Core objective: make local price differences visible.

Is This a Fair Price?

04 / Platform overview

AI-powered price intelligence designed to bring transparency to physical retail.

Shopping receipts

Read item names, prices and store details from offline purchases.

Product price tags

Capture shelf prices with a camera.

Online store pricing

Potential online price context; availability varies.

CartLens AI

Match products across sources, check confidence and grow local coverage through repeated observations.

Comparisons depend on reliable matches and relevant local observations. If current data is insufficient, the experience needs to say so clearly.

From a Receipt to Nearby Prices

05 / How it works

Scan → Match → Build → Compare

01 / Scan

Take a photo of a receipt or shelf tag. CartLens reads item names, prices, available metadata and the store.

02 / Match

Turn stores’ shortened item names into common product identities, checking package sizes and units.

03 / Build

Add authorized contributed prices with their store and original purchase date. Flag uncertain matches for review.

04 / Compare

Compare your items with prices shared by nearby shoppers. Save the trip for your next shop.

An archived trip alone is not a completed price comparison. Uploading an old receipt does not make its price current.

Market Opportunity

06 / Market dynamics

A paid household planning model at $6.99/month ($83.88/year).

$10.84B / TAM

129.227 million U.S. households × $83.88/year. Source in the deck: Census QuickFacts, 2020–2024.

$1.03B / SAM

13.548 million California households × 91% smartphone-reach proxy × $83.88/year. Sources in the deck: U.S. Census Bureau and Pew Research.

~$839K / Annual revenue case

10,000 paid accounts × $6.99/month. Illustrative volume, not validated market capture.

These figures frame potential scale; they do not establish demand or forecast subscriber acquisition. Grocery, pharmacy, apparel and other in-store purchases are possible use cases.

Strategic Positioning

07 / Competitive landscape

Stores use different names for the same item. Matching them makes price comparisons useful.

Building a potential data advantage

Link retailers’ item names to canonical products in a unified catalog. Use reviewed matches and corrections to train a specialized model, then connect those identities to fresh local price evidence.

Competitor features reflect the source deck’s vendor research, not independently tested benchmarks. The data advantage remains a hypothesis; OCR and basket comparison are not unique to CartLens. Planned training must prove better matching or lower processing costs.

Full Interactive Demo

08 / Product experience

Explore receipt scanning, item matching and nearby price comparisons.

The workflow

Scan or upload a receipt, review extracted items and prices, correct an item if needed, compare matching nearby prices, adjust distance and price-age settings, and save the trip.

Demonstration prices illustrate the interface. They do not establish current availability, production accuracy or realized savings.

Business Model & Pricing

09 / Current plans

Public plans remain separate from the proposed pricing experiment.

Free / $0 per month

No time limit. 3 archived trips per rolling 30 days, 50 MB storage, receipt and shelf-tag scanning.

Pro / $6.99 per month

$5.83/month billed yearly. Unlimited archived trips, 2 GB storage, AI insights and priority customer support.

Ultra / $9.99 per month

$8.33/month billed yearly. Everything in Pro, 10 GB storage and CartAssistant.

Proposed test: $4.99 / $6.99 / $9.99 monthly. In six survey responses to the current-plan question, four chose Free and two declined every plan; none chose Pro or Ultra. These are stated preferences, not purchases. The $6.99 forecast input is not adopted pricing.

Start With Useful Local Prices

10 / Go-to-market

Recruit through creators and community groups, then test paid subscriptions after a useful comparison.

SF Bay Area & Central Valley

Build a shared price map from authorized receipts across 20+ stores in one pilot zone. Keep old prices clearly dated.

Regional budgeting creators

Recruit local micro-creators to introduce CartLens to their audiences.

Community groups & leaders

Use distinct group and launch codes to attribute signups and paid upgrades.

Introductory offer test

Proposed: 25% off the first three versus six paid months. Track purchases, net revenue and renewals at full price.

Creators, partnerships and discounts are prospective. Review incremental seeding and discount costs before adoption; the operating baseline is unchanged.