Integrations

Paz finds the gaps costing you Shopping visibility and sales, drafts the fix from your own information, and publishes it to Shopify, Adobe Commerce, Salesforce Commerce and Google Merchant Center once you approve. Google Analytics 4, Search Console, NetSuite, Akeneo, Gorgias, Klaviyo and the other systems below are read to decide what matters first. You can start from your product pages before connecting anything.

Book a demoHow it works
  • Shopify
  • Adobe Commerce
  • Salesforce Commerce
  • Google Merchant Center
  • Google Analytics 4
  • Google Search Console
  • Snowflake
  • BigQuery
  • Oracle NetSuite
  • SAP
  • Microsoft Dynamics 365
  • Akeneo

What Paz connects to

Each connection is confirmed for the workflows you choose. Read and write permissions are separate, and any change can stay a handoff for your team instead of a direct write.

Ecommerce platforms

  • Shopify

    Paz reads your products and metafields, checks them against the shopping questions, GMC issues and traffic it tracks, and writes the approved fix back to the product record so storefront and every synced channel carry it.

    Publishes approved changes
    Example and permissions
    Example: A priority collection is missing capacity and material on most of its products. Paz drafts both from your catalog and PDP copy, you review the drafts product by product or approve the batch, and the metafields update in Shopify and flow to the feed.
    Paz reads
    Products, variants, descriptions, metafields and collections.
    Paz writes, once you approve
    Product fields and metafields you allow, written to the product record.

  • Adobe Commerce

    Paz reads attributes and categories across your store views, finds the gaps behind disapprovals and lost visibility, and writes approved attribute fixes back so downstream feeds stop re-importing the problem.

    Publishes approved changes
    Example and permissions
    Example: One store view is missing the material attribute the other views carry for the same products. Paz proposes the values from the sibling view, you approve, and the attribute is written back before the next feed run.
    Paz reads
    Products, attributes and categories.
    Paz writes, once you approve
    Product attributes you allow, written back to the product record.

  • Salesforce Commerce

    Paz reads the catalog and attributes, ties each gap to the channel or question it hurts, and writes approved attribute fixes back so site search, feed and PDP say the same thing.

    Publishes approved changes
    Example and permissions
    Example: Size charts exist in the catalog but not in the attribute the feed maps. Paz proposes the mapping fix per product, you approve, and the feed picks it up on its next sync.
    Paz reads
    Product catalog and attributes.
    Paz writes, once you approve
    Product attributes you allow, written back to the product record.

  • Product feed or CSV

    Paz reads your feed or product list on a schedule, runs the same checks it would on a connected platform, and drafts the corrections as a file your team loads into the source.

    Paz drafts the fix, you apply it
    Example and permissions
    Example: Your WooCommerce export shows a few hundred products with one-line descriptions. Paz drafts fuller descriptions from the attributes in the export, you approve, and your team imports the file.
    Paz reads
    Your feed file or product URL list, on a schedule.
    Paz writes, once you approve
    Nothing directly. Fixes go to the source your feed is built from.

Channels and distribution

  • Google Merchant Center

    Paz reads disapprovals, warnings and attribute coverage, traces each to the product data behind it, pushes approved attributes and product Q&A, and re-checks Merchant Center after Google's review to report what cleared.

    Publishes approved changes
    Example and permissions
    Example: A dozen bestsellers are disapproved for missing color. Paz finds the value on each PDP, you approve, Paz pushes the attribute and reports after Google's re-review which disapprovals cleared.
    Paz reads
    Product status, disapprovals, warnings and attribute coverage.
    Paz writes, once you approve
    Approved product attributes, including product details and product Q&A, applied to the products that changed.

  • ChatGPT Ads

    Paz checks which products meet the ChatGPT Ads feed spec, tells you why the rest fail, and builds the feed from approved product data for the ones you choose.

    Builds the ChatGPT Ads feed
    Example and permissions
    Example: Most of your top products are eligible; the rest are missing a brand field. Paz drafts the values, you approve, and the compliant feed is built for the campaign.
    Paz reads
    Which products meet the feed requirements, and why the rest do not.
    Paz writes, once you approve
    A product feed built from approved data for the products you select.

Analytics and search

  • Google Analytics 4

    Paz reads sessions, channels and product performance, and when a metric moves it pairs the change with what it sees in Search Console, Merchant Center and your catalog to propose a cause and a fix.

    Read only
    Example and permissions
    Example: Organic sessions to a category fall by a fifth in a week. Paz finds the drop sits on the PDPs that lost a rich result, drafts the structured-data fix for approval; publishing runs through your Shopify or platform connection.
    Paz reads
    Sessions, channels, landing pages and product performance.

  • Google Search Console

    Paz reads queries, pages, indexing and search appearance, so a traffic change can be traced to the pages and queries that lost ground and the product content that would recover them.

    Read only
    Example and permissions
    Example: Impressions hold but clicks fall on a set of product pages. Paz shows the titles that changed in the last release and drafts titles that restore the product name and key attribute.
    Paz reads
    Search performance, indexing status, search appearance and sitemaps.

  • Snowflake

    Paz reads the order, return and margin tables you share, so proposed work is ranked by contribution and return rate rather than traffic alone. Sharing a view is a one-time step for whoever owns your warehouse.

    Read only
    Example and permissions
    Example: Two high-traffic products also have a high return rate. Paz reads the return reasons, finds that most cite sizing, and proposes the sizing details missing from their PDPs before promoting them further.
    Paz reads
    The views you expose: orders, returns, product margin and cohorts.

  • BigQuery

    Paz reads your GA4 export and order tables together, so a traffic change can be tied to the revenue and margin behind it before Paz decides whether it is worth fixing.

    Read only
    Example and permissions
    Example: A landing page loses sessions but revenue is flat. Paz shows the lost traffic was non-converting, deprioritizes the page and moves to a gap that affects paying visitors.
    Paz reads
    The datasets you expose: orders, returns, product margin and cohorts.

ERP and inventory

  • Oracle NetSuite

    Paz reads item availability and product master data, so it stops recommending promotion of items you cannot ship, flags stock-outs that explain lost sales, and drafts PDP attributes from the master record.

    Read only
    Example and permissions
    Example: A priority product is missing from AI shopping answers. NetSuite shows it is out of stock; Paz parks the visibility work and flags the stock-out as the cause instead.
    Paz reads
    Item availability, allocation and product master data.

  • SAP

    Paz reads material availability and product master data, so recommendations respect what your supply chain can fulfil and product facts match the record of truth.

    Read only
    Example and permissions
    Example: A campaign product is short in two plants. Paz holds its promotion proposals for those regions and prioritizes catalog fixes on the products with stock.
    Paz reads
    Material availability, allocation and product master data.

  • Microsoft Dynamics 365

    Paz reads inventory and product master data, so catalog fixes use the master values and promotion proposals track what is actually in stock.

    Read only
    Example and permissions
    Example: Dynamics shows dimensions for a range of products that your PDPs omit. Paz drafts the PDP and feed attributes from the master data for your approval.
    Paz reads
    Item availability, allocation and product master data.

PIM and DAM

  • Akeneo

    Paz reads families, attributes and completeness, treats Akeneo as the source of truth, and proposes fixes to the PIM gaps that are causing disapprovals and missing answers downstream.

    Read only
    Example and permissions
    Example: Completeness for one family is low because a channel-required attribute is empty. Paz lists the products, drafts the values from your PDPs and hands them to your PIM owner.
    Paz reads
    Product attributes, families, categories and completeness.

  • Salsify

    Paz reads product content and digital-shelf readiness, so channel requirements decide which gaps are fixed first and fixes go back through your content workflow.

    Read only
    Example and permissions
    Example: Salsify flags a set of products as not ready for a retailer channel. Paz cross-checks with GMC and prioritizes the ones that are also disapproved in Shopping.
    Paz reads
    Product content, digital shelf requirements and readiness.

  • Bynder

    Paz reads asset metadata, usage rights and product links, so a product is not pushed to a channel without approved, rights-cleared imagery.

    Read only
    Example and permissions
    Example: A launch collection is missing the second image GMC prefers. Paz finds the approved assets in Bynder and proposes the image mapping for each product.
    Paz reads
    Asset metadata, usage rights and product links.

  • Cloudinary

    Paz reads image tags and renditions, so listings are checked for the image formats and views each channel requires before the product is promoted there.

    Read only
    Example and permissions
    Example: Several products have only a lifestyle shot. Paz flags them against the channel's white-background requirement, proposes which existing renditions to map for each product, and your team approves the mapping.
    Paz reads
    Asset metadata, tags and product links.

Customer feedback and returns

  • Gorgias

    Paz reads ticket topics and the products they mention, so recurring pre-sale questions and complaints become the product details and Q&A your PDPs are missing.

    Read only
    Example and permissions
    Example: Dozens of tickets a month ask whether a bag fits a 16-inch laptop. Paz drafts the dimension and a Q&A entry for the PDP and feed, for your approval.
    Paz reads
    Ticket topics and the products they mention.

  • Zendesk

    Paz clusters tickets by product and topic, so fit, compatibility and care questions turn into PDP fixes and answers instead of repeat tickets.

    Read only
    Example and permissions
    Example: Compatibility questions cluster on one accessory line. Paz drafts a compatibility attribute and Q&A per product from your spec sheets.
    Paz reads
    Ticket topics and the products they mention.

  • Yotpo

    Paz reads reviews, ratings and shopper questions, so review themes become the product details shoppers ask for and low ratings point to expectation gaps to fix on the page.

    Read only
    Example and permissions
    Example: Reviews on a jacket repeatedly say it runs small. Paz proposes a fit note and updated size guidance on the PDP and in the feed.
    Paz reads
    Reviews, ratings and product questions.

Marketing and advertising

  • Google Ads

    Paz reads spend and conversions by product group, so catalog and feed fixes go first to the products you are paying to promote and wasted spend on broken listings shows up.

    Read only
    Example and permissions
    Example: Your highest-spend product group has disapproved items in it. Paz ranks their fixes first and shows the spend that was landing on them.
    Paz reads
    Campaign, product-group and spend performance.

  • Meta Ads

    Paz reads which catalog products your dynamic ads are spending on, so title and price fixes reach those products first and the ad shows what the PDP shows.

    Read only
    Example and permissions
    Example: Ads are serving an old price on a handful of products. Paz traces it to a stale catalog field, proposes the fix in Shopify, you approve, and Paz checks that the Meta catalog picked up the new price on its next refresh.
    Paz reads
    Catalog, campaign and spend performance.

  • Klaviyo

    Paz reads your campaign calendar and segment response, so product fixes land before the products you are about to email go out, and each campaign's clicks on fixed products tell Paz which pages to check next.

    Read only
    Example and permissions
    Example: Next week's campaign features a dozen products; a few have thin PDPs. Paz drafts the missing details this week so the landing pages are ready before send.
    Paz reads
    Campaign calendar, segments and product-level response.

  • Another system?

    Tell us the system and what you want Paz to help with, and we will set up the connection with you.

    Talk to us about your systems

AI answers Paz checks

Paz checks what ChatGPT, Google AI Mode and Perplexity return for your shopping questions. No account connection is needed.

Your priorities, brand guidance and plans are added directly during setup. They are not an integration, and they shape which work Paz proposes first.

Test Paz on your store

Bring the systems you run. Paz starts from your PDPs, finds the gaps and publishes what you approve to the platforms you connect.

Book a demo