Count, identify, or verify physical items from camera or image input. This comparison covers products mapped to the task specifically in Retail & Ecommerce.
Companies collected
11
Products compared
16
Industries observed
19
Market observation
Count Physical Objects has a distinct market in Retail & Ecommerce
This pool mixes merchandise recognition with shopper counting, which serve different buying needs. Mashgin supplies the clearest deployment claim. No numeric prices are supplied; low disclosure scores reflect evidence gaps, not proven poor quality.
SOTA2 collected 11 companies and 16 products with explicit evidence for this task in Retail & Ecommerce. That vertical evidence is what makes this more useful than a general product list.
The products still have to prove task fit, adoption, product maturity, and pricing—the industry label alone does not improve their position.
AI-powered checkout using computer vision and machine learning to instantly identify items without barcodes.
Why #1
83/100 evidence score
Computer vision identifies checkout items without barcodes in self-checkout or hosted workflows. The company reports 5,000+ locations and 40M+ monthly users.
Counts specific objects from images automatically and returns structured outputs including total count, confidence score and optional overlays.
Why #3
67/100 evidence score
Automatically counts specified objects in images and returns totals, confidence scores and optional overlays. Retail is a stated company market, and a free trial is listed.
DescriptionPrimary Use CasesFree Plan Or Trial
Task fitStrong
Adoption evidenceLimited
Product evidenceStrong
PricingLimited
Market fitStrong
Best for
Image-based object counting that requires structured results.
Pricing
Pricing not published
What to verify
Retail positioning is broad; supported retail object classes, deployment results and paid pricing are unspecified.
AI-powered self-service scale solution that retrofits traditional scales to automatically recognize fresh products including fruits, vegetables, grains, and nuts—even when bagge...
Why #4
66/100 evidence score
Retrofits traditional scales to automatically recognize fresh grocery products, including bagged fruits, vegetables, grains and nuts.
DescriptionPrimary Use Cases
Task fitStrong
Adoption evidenceLimited
Product evidenceStrong
PricingLimited
Market fitStrong
Best for
Grocers automating fresh-product identification at existing scales.
Pricing
Pricing not published
What to verify
Camera requirements, measured recognition accuracy, customer deployments and pricing are not supplied.
Uses artificial intelligence to scan products on shelves with high accuracy, aiming to increase availability and sales while lowering operational costs.
Why #5
66/100 evidence score
AI scans products on shelves, supported by company evidence describing computer-vision shelf digitization and retail availability workflows.
DescriptionEmployee RangeY Combinator
Task fitStrong
Adoption evidenceLimited
Product evidenceModerate
PricingLimited
Market fitStrong
Best for
Retail shelf scanning to support on-shelf availability.
Pricing
Pricing not published
What to verify
Unit-count granularity, measured accuracy and customer deployment totals are not documented; company size and YC participation are only indirect adoption signals.
AI marketing solution that analyzes CCTV footage to measure store performance and visitor data, including people counting, visitor flow, behavior, product interest, and popup-st...
Why #7
64/100 evidence score
Analyzes store CCTV for people counts, visitor flow and behavior. Korean PIPA compliance is stated.
DescriptionPrimary Use CasesCompliance
Task fitStrong
Adoption evidenceLimited
Product evidenceStrong
PricingLimited
Market fitStrong
Best for
Retail and popup-store people counting with visitor behavior analytics.
Pricing
Pricing not published
What to verify
Addresses visitor analytics rather than merchandise counting; counting benchmarks, deployment scale and pricing are unspecified.
Retail merchandising and activation combining cutting-edge technology with on-demand workforce, setting a new standard for data-driven performance.
Why #8
61/100 evidence score
Explicit use cases include shelf image recognition, on-shelf availability tracking and pricing/promotional compliance, supported by the company's computer-vision focus.
DescriptionPrimary Use CasesSocial Following
Task fitStrong
Adoption evidenceLimited
Product evidenceModerate
PricingLimited
Market fitStrong
Best for
Retail merchandising teams verifying shelf conditions from images.
Pricing
Pricing not published
What to verify
The description combines technology with workforce services; software boundaries, counting granularity and pricing remain unclear.
