Integrations

A Paz expert uses the systems you connect to investigate product information and make improvements. Shopify, Adobe Commerce, Salesforce Commerce and Google Merchant Center support agreed publishing actions. Analytics, ERP and PIM connections give us more context for the work. You can start from your public product pages.

Talk to usHow 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. We agree which updates Paz handles directly and which your team prefers to apply.

Ecommerce platforms

  • Shopify

    Paz reads your products and metafields, checks them against the shopping questions, GMC issues and traffic it tracks, and writes approved fixes back to the product record. Updates reach your storefront and connected feeds through the field mappings you have configured.

    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. Connected feeds can pick up those values where the fields are mapped.
    Paz reads
    Products, variants, descriptions, metafields and collections.
    Publishing scope
    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.

    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, our experts check the change against agreed guidelines, and the attribute is written back before the next feed run.
    Paz reads
    Products, attributes and categories.
    Publishing scope
    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.

    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, our experts check the change against agreed guidelines, and the feed picks it up on its next sync.
    Paz reads
    Product catalog and attributes.
    Publishing scope
    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.

    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, our experts check the change against agreed guidelines, and your team imports the file.
    Paz reads
    Your feed file or product URL list, on a schedule.
    Publishing scope
    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.

    Example and permissions
    Example: A dozen bestsellers are disapproved for missing color. Paz finds the value on each PDP, our experts check the change against agreed guidelines, then Paz pushes the attribute and reports after Google's re-review which disapprovals cleared.
    Paz reads
    Product status, disapprovals, warnings and attribute coverage.
    Publishing scope
    Approved product attributes, including product details and product Q&A, applied to the products that changed.

  • ChatGPT Shopping

    Paz builds your product feed to OpenAI’s spec and pushes it on a schedule so your products can appear in ChatGPT shopping results, then reports what was accepted. From the same catalog, Paz can also publish a branded ChatGPT App and a UCP endpoint for stores that want their own experience in ChatGPT.

    Example and permissions
    Example: Your Adobe Commerce catalog has no route into ChatGPT. Paz maps the catalog to the OpenAI feed, flags the products missing a required field, our experts check the drafted values against agreed guidelines, and the feed is pushed and refreshed on the agreed schedule.
    Paz reads
    Catalog, product content and availability.
    Publishing scope
    The OpenAI product feed on an agreed schedule through the supported submission path. Optionally a branded ChatGPT App and a UCP endpoint.

  • 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.

    Example and permissions
    Example: Most of your top products are eligible; the rest are missing a brand field. Paz drafts the values, our experts check the change against agreed guidelines, and the compliant feed is built for the campaign.
    Paz reads
    Which products meet the feed requirements, and what the rest are missing.
    Publishing scope
    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. We check a meaningful decline against Search Console, Merchant Center and catalog information to investigate possible causes and choose a response.

    Example and permissions
    Example: Organic sessions fall for a priority category. We check the affected PDPs and Search Console evidence. If missing product details warrant a correction, Paz drafts supported field updates. A markup or template issue comes to you with the fix spelled out. A Paz expert tracks subsequent traffic and conversion against the baseline.
    Paz reads
    Sessions, channels, landing pages and product performance.

  • Google Search Console

    Paz reads queries, pages, indexing and search appearance. We identify the pages and queries that lost ground, checks possible explanations and prepares supported product-content changes when the evidence warrants them.

    Example and permissions
    Example: Impressions hold but clicks fall on a set of PDPs. We compare the queries and current titles with product sources. If useful details are missing, Paz drafts corrected titles for the permitted commerce-platform fields, then checks clicks over the agreed comparison period.
    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 as well as traffic. Sharing a view is a one-time step for whoever owns your warehouse.

    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.

    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 recommends promotion only for items you can ship, flags stock-outs that explain lost sales, and drafts PDP attributes from the master record.

    Example and permissions
    Example: A priority product is missing from AI shopping answers. NetSuite shows it is out of stock. We use availability to prioritize work on products shoppers can buy now.
    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.

    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.

    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 checks against agreed guidelines.
    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.

    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.

    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 reaches a channel with approved, rights-cleared imagery.

    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.

    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.

    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 checks against agreed guidelines.
    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 that settle the repeat tickets.

    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.

    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.

    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 product-content corrections can be prioritized for those products.

    Example and permissions
    Example: A promoted product has an incomplete title in Shopify. Paz drafts a title using verified product details for approval and publishing through the Shopify connection. The Meta Ads connection stays read only.
    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.

    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 and scores your visibility there. Google AI Overviews, Gemini and Copilot are checked as well. No account connection is needed.

Your priorities, brand guidance and plans are added during setup and shape which work Paz proposes first.

Put Paz to work for your store.

Tell us where you need help with SEO, AI visibility, product content or feeds. We’ll discuss your priorities and recommend where to start.

Talk to us