Product data for AI commerce

Your catalog can sell more, before you spend more on ads.

Findmerce structures product data, feeds and product pages so offers are easier to understand, compare and match by search engines and AI recommendation systems.

No ranking promises. We show specific data gaps and specific improvements.

Findmerce Product Readiness sample analysis
Example customer query “Office chair for 8h/day for a 190 cm tall person under PLN 1,500”
XTR Pro Black
Recommended height missing • armrests not described • material unclear
Comfort Mesh 4D
Height 170–195 cm • load 150 kg • 4D armrests • mesh
Readiness score 68 / 100
Biggest gap: purchase attributes
5 productsin the free mini audit
No admin accessduring the initial diagnosis
Data instead of promisesa concrete scope of changes
PL / EN / DEready for international markets
What we actually improve

Catalog first. “AI visibility” comes later.

If a product has incomplete specifications, unclear variants or inconsistent data between the page and the feed, even the best recommendation system has limited ability to match the offer correctly.

01 / Product data

Product information

Titles, attributes, materials, dimensions, use cases, limitations, variants and specifications needed for real product comparison.

02 / Feeds

Catalog consistency

We check whether data on the site, in the feed and in product structure are consistent and whether the most important information is complete.

03 / Technical

Technical layer

Core Product / Offer schema, indexability, product-page clarity and technical barriers that make catalog analysis harder.

1

Find the gaps

We identify missing data, unclear names, variants and inconsistencies.

2

Map intent

We identify what information is needed for queries such as “best X under Y for Z”.

3

Prepare improvements

We deliver specific changes to titles, descriptions, attributes, feeds and structure.

4

Verify implementation

After changes, we verify whether the catalog is more consistent and whether the identified gaps have actually been closed.

Example

Less marketing copy. More purchase-relevant information.

This is not about stuffing keywords. It is about making product names and data answer the questions that genuinely come up before purchase.

Before XTR PRO BLACK

A high-quality chair for demanding users. Modern design and maximum comfort.

After XTR Pro Black — ergonomic office/gaming chair, 150 kg load, 4D armrests

We also add attributes such as recommended user height, material, adjustment range, warranty and complete variants.

We do not show fabricated clients, results or testimonials.

We will publish our first case studies only after real implementations. For now, we prefer to show our method and quality of work rather than pretend to be a large agency.

See the process →
First step

Send us your store. We will start with 5 products.

No admin access, no long contract and no commitment. You will receive an initial diagnosis and an example of a concrete improvement.

Free mini audit