Agentic Commerce Readiness · CPG & Retail

Is your product data ready for agentic commerce?

AI shopping agents decide which products get recommended, and they surface only the products whose data they can read and trust. This is a readiness check across the three attribute types they rely on, Core, Contextual and Custom, at the UPC level.

Take the readiness check See the proof

Core Custom Contextual

The Shift

Discovery moved from browsing to querying.

Consumers no longer scroll category pages. They ask an AI agent to find the best high-protein snack under 200 calories. An agent does not browse. It queries, and it recommends only the products whose data it can parse, evaluate, and trust.

58%

of consumers have replaced traditional search engines with AI for product research, up from 25% in 2023.

Capgemini Research Institute, Jan 2025

4,700%

growth in AI-driven traffic to US retail sites year-over-year in 2025, the fastest-growing channel in ecommerce history.

Adobe Digital Insights, Aug 2025

$20.57B

in US AI commerce spending in 2026 alone, nearly four times the 2025 figure. The inflection is already here.

eMarketer, Dec 2025

The Short Answer

What product data do AI shopping assistants use?

Three kinds: Core attributes that say what the product is, Contextual attributes that describe how shoppers use it and talk about it, and Custom attributes that apply a brand or retailer's own definitions, such as what counts as high protein. Agents recommend the products whose data answers the shopper's question, and every attribute carries its source, so an agent can tell a label claim from a review.

One Data Layer

Connected at the product level.

We join the data you already have to one product record at the UPC level, so every team works from the same description.

Sales & POS

Trend signals

Surveys

Panels

UPC-level Product Layer

Sales, trends, surveys and panels each tell you something true. None of them share a product-level structure, so the layer where decisions get made stays invisible. Harmonya joins everything you already have at the UPC level into one shared product layer, so every team works from one source of truth.

The Framework

Three layers of agentic commerce readiness.

Each one builds on the layer beneath it. The lowest sets the ceiling for everything above.

Each layer builds on the one beneath it. The lowest sets the ceiling for everything above.

Can AI agents read your products?

Core

What the product is, straight from the pack: category, size, ingredients, nutrition, certifications and on-pack claims. Agents exclude incomplete data rather than guess, so every SKU needs these fields machine-readable at the UPC level.

Do you describe products the way shoppers ask for them?

Contextual

The benefits, occasions, audiences, taste and texture shoppers attach to a product, drawn from listings and reviews. When someone asks an agent for a snack that keeps them full, this is the layer that answers.

Is your own view of the category applied to every product?

Custom

The demand spaces, need states and claim thresholds you define, applied to every UPC in the category and kept current as products change. Once they hold across the category, each one can be joined to sales and sized in dollars.

Readiness Check

Where does your agentic commerce readiness stand today?

Answer honestly: yes or no, no partial credit. Eighteen questions across the three layers. Your score builds live, and the path forward updates as you go.

Layer 01Core 0 / 6

You have structured attribute data at the UPC level for every active SKU, not just your top sellers.No

Your ingredients, claims, certifications, and allergens are machine-readable, not embedded in a label image.No

Your product data is updated within 30 days of any formula, packaging, or claims change.No

Your attributes are consistent across DTC, Amazon, Walmart.com and Target.com using one taxonomy.No

You can identify, right now, which SKUs have incomplete or missing attribute coverage.No

Your diet and lifestyle claims (keto, vegan, non-GMO, high-protein) are standardized at the UPC level.No

Layer 02Contextual 0 / 6

Your products carry benefit, occasion and audience attributes, not only what is printed on the pack.No

You analyze consumer reviews at scale, not just star ratings or manual samples.No

Your reviews are connected to specific product attributes, not rolled into one sentiment score.No

You track sentiment by attribute, so you know whether a complaint is about taste, texture, price or a claim.No

Your product page copy uses the words shoppers use for your products.No

You can tell, for every attribute, whether it came from the brand or from a shopper.No

Layer 03Custom 0 / 6

You have defined the demand spaces or need states your category runs on.No

Every UPC in the category, including competitors', is mapped to those definitions.No

Your claim thresholds, such as what counts as high protein, are written down and applied to every product.No

Your definitions stay current as products are reformulated, without a manual recode.No

You can put a dollar value on each demand space, by category and by retailer.No

You can trace a specific revenue gap back to an attribute or demand space you are missing.No

Based on your answers

Begin the check above

Answer the 18 questions above and your personalized readout will build here, including which layer to prioritize first.

1

Start here

Build the Core foundation

Make every SKU machine-readable at the UPC level so AI agents can find and evaluate it.

2

Start here

Add the Contextual layer

Describe products the way shoppers do, with benefits, occasions and taste drawn from listings and reviews.

3

Start here

Apply your Custom view

Define your demand spaces and claim thresholds, apply them to every UPC, and size each one in dollars.

Proven In Category

What clients have measured.

Food & Beverage · Product Catalog

>25× ROI

on maintained product catalog work for a top-five F&B manufacturer, including $20M a year saved through vendor contract renegotiation.

Global Snacks · Demand sizing

$300M+

in quantified revenue opportunity across 5,000+ SKUs, including a $130M Bold & Spicy gap and a $475M Plant-Based theme.

RTD Coffee · Review analysis

$1.2B+

category decoded across 8 competitors, mapping the exact positioning territories each brand owns in consumer language.

See Where You Stand

See your category, sized in dollars.

In a demo we walk through your category, which demand spaces are growing and how your coverage compares with the competitive set.

Get a demo