Google Shopping API: AI-Powered Feed Optimisation
Automate your Google Shopping product feeds with our REST API. AI optimises titles, descriptions, and categories — export to Merchant Centre in one click.
Manual feed management is costing you revenue
Hours wasted on manual feed updates, silent disapprovals from stale data, and the complexity of managing feeds across multiple channels.
Hours wasted on manual feed management
Updating product titles, descriptions, and categories by hand takes days of work every month. For a catalogue of 5,000+ products, even small changes like updating seasonal keywords or fixing formatting issues become a multi-day project. That's time your team could spend on strategy, campaign optimisation, or expanding to new channels.
Disapprovals from poor titles & missing GTINs
Incomplete product data and poorly written titles cause silent disapprovals in Google Merchant Centre. Your products stop appearing in Shopping results, but the Merchant Centre diagnostics don't always make it obvious why. Missing GTINs, incorrect categories, truncated titles, and policy violations can quietly pull hundreds of listings from your campaigns without triggering a single alert.
Stale data costing revenue
Outdated prices, descriptions, and categories hurt CTR and conversion rates across every channel you sell on. A product with last season's description or yesterday's price creates a poor shopping experience — and Google's algorithms notice. By the time you see the impact in your campaign reports, you've already lost weeks of revenue to competitors with fresher, more accurate listings.
Three steps to automated feed optimisation
Import your feed via API, let AI optimise it, then export to Google Merchant Centre and 200+ other channels.
Import feed via API
Upload your product feed from CSV, XML, JSON, or a hosted URL using a single API call. The importer handles parsing, validation, and field mapping automatically — it detects your column structure and maps it to standard product attributes. Most Shopify, WooCommerce, Magento, and BigCommerce feeds work out of the box without configuration.
AI optimises titles, descriptions & categories
Trigger AI optimisation via the API to rewrite product titles for maximum click-through rate, enhance descriptions with relevant details, and map every product to the correct Google taxonomy category. The AI analyses your existing product data and applies consistent rules across the entire feed. A catalogue of 3,000 products can be fully optimised in a single API call.
Export to Google Merchant Centre + 200+ channels
Generate a compliant feed URL for Google Merchant Centre with one API call. The exported feed meets Google's format requirements automatically — correct attribute naming, required fields, and taxonomy codes. Set up additional exports for Meta Catalogue, TikTok Shop, Amazon, eBay, and 200+ other channels, each formatted to that channel's specifications.
Complete REST API for feed management
Everything you need to automate your Google Shopping feeds.
REST API with full CRUD
Create, read, update, and delete brands, feeds, and products with standard HTTP methods. The API follows RESTful conventions with JSON request/response bodies, Bearer token authentication, and predictable URL patterns. Every resource supports pagination, filtering, and sorting for efficient data access at scale.
AI title & description enhancement
Trigger AI optimisation via a single endpoint to rewrite product titles and descriptions for maximum CTR and relevance. The AI follows Google Shopping best practices — leading with brand and product type, including key attributes like size, colour, and material, and using keywords that match real search queries. Custom instructions let you control the optimisation style per feed or product type.
Google category mapping
Automatic mapping to Google's product taxonomy with over 6,000 categories. The system analyses product attributes, titles, and descriptions to assign the most specific category available. This eliminates one of the most common Merchant Centre disapproval causes — products assigned to overly broad or incorrect categories that don't match their actual type.
Feed audit for disapprovals
Scan your entire feed for common issues with a single API call. The audit checks for missing GTINs, incorrect categories, broken image URLs, incomplete required attributes, and potential policy violations. Results come back as a structured JSON response with the affected product IDs and specific issues, making it easy to automate bulk fixes.
Bulk operations (5,000+)
Update thousands of products in a single API call with no rate limits on bulk operations. Import, update, optimise, and export at scale — the API is designed for catalogues of 100,000+ products. Bulk operations run asynchronously so your application doesn't need to wait for completion.
Multi-brand support
Manage feeds for multiple brands from one API key. Each brand is a separate entity with its own feeds, products, and export configurations. This is ideal for agencies managing client accounts or retailers with multiple brand lines — each brand's data is isolated while your integration code stays unified.
Scheduled exports
Set up recurring exports to Google Shopping, Meta, TikTok, Amazon, and 200+ other channels via the API. Define the schedule, target channels, and format once — exports run automatically at the specified intervals. You can also trigger on-demand exports when you need an immediate update.
Custom rule engine via API
Define rules programmatically that run on every feed sync. Filter out-of-stock products, set minimum price thresholds, override titles for specific categories, append tracking parameters, or apply channel-specific transformations. Rules are managed through the API and apply consistently without manual intervention.
API Examples
Integrate with the AI Shopping Feeds REST API
Import your feed, trigger AI optimisation, and export to Google Shopping — all via simple API calls.
bash# Import a product feed
curl -X POST https://app.aishoppingfeeds.com/api/v1/feeds \
-H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
-d '{"name": "Google Shopping Feed", "source_url": "https://example.com/products.xml"}'
# Trigger AI optimisation
curl -X POST https://app.aishoppingfeeds.com/api/v1/feeds/{feed_id}/optimise \
-H "Authorization: Bearer your-api-key" \
-d '{"fields": ["title", "description", "google_category"]}'
# Export to Google Shopping
curl -X POST https://app.aishoppingfeeds.com/api/v1/feeds/{feed_id}/export \
-H "Authorization: Bearer your-api-key" \
-d '{"channel": "google_shopping"}'Real-world use cases
How developers and operations teams use the API to automate Google Shopping feed management.
Developer integrating feed management into a custom platform
A developer at a marketplace aggregator is building a platform that lets sellers list products across multiple channels. They need a feed management API they can integrate directly into their platform's backend — not a standalone tool their sellers have to log into separately.
- 1Create a brand per seller using the REST API, keeping each seller's product data isolated.
- 2Build an import pipeline that pushes product data from the platform's database to AI Shopping Feeds via the API whenever a seller updates their catalogue.
- 3Call the AI optimisation endpoint to enhance titles and descriptions for Google Shopping, Meta, and Amazon simultaneously.
- 4Generate channel-specific export URLs for each seller and display them in the platform's dashboard.
- 5Set up scheduled exports so each seller's feeds are refreshed daily without manual action.
- 6Run weekly feed audits via the API and surface disapproval warnings in the platform's seller notifications.
The platform launched multi-channel feed management as a built-in feature without building their own feed engine. Seller onboarding to Google Shopping dropped from 5 days to 4 hours. The API handles 150,000+ products across all seller accounts with zero performance issues.
Agency building a white-label feed management tool
A digital marketing agency wants to offer branded feed management as part of their service package. They need an API backend they can wrap with their own UI and sell to clients as 'their' feed optimisation platform.
- 1Create an agency-level account with a master API key and multi-brand support on the Pro plan.
- 2Build a custom frontend that connects to the AI Shopping Feeds REST API under the agency's domain.
- 3Implement client onboarding: create a brand per client, import their feed from their Shopify/WooCommerce store, and run initial AI optimisation.
- 4Expose scheduled export configuration and feed audit results through the custom UI.
- 5Use the bulk operations endpoint to offer 'one-click optimise all clients' functionality for the agency's account managers.
The agency launched their white-label feed tool in 6 weeks instead of the estimated 6 months to build from scratch. They now manage 45 client feeds through their branded interface, charge a monthly platform fee per client, and have created a new revenue stream alongside their campaign management services.
Operations team automating bulk product updates
An e-commerce operations team at a sports equipment retailer manages 12,000 SKUs across Google Shopping, Amazon, and eBay. Seasonal product transitions, price changes, and new product launches require frequent bulk updates that currently take the team 2-3 days each time.
- 1Set up an automated pipeline that calls the AI Shopping Feeds API whenever the retailer's product database updates (triggered by webhook from their ERP).
- 2On each update, the pipeline imports the changed products, triggers AI optimisation for any new or modified titles, and maps updated categories.
- 3Run a feed audit after each import to catch any new issues introduced by the product changes.
- 4Schedule exports to Google Shopping (daily at 2 AM), Amazon (daily at 3 AM), and eBay (weekly on Monday).
- 5Generate monthly feed health reports using the audit API for the operations team's review.
Bulk product updates now happen automatically within 2 hours of the ERP change — down from 2-3 days of manual work. The team eliminated their feed management backlog entirely. Google Shopping disapprovals dropped from 3.8% to 0.4%, and the operations team repurposed 15 hours per week to merchandising and pricing strategy.
How the API works in depth
Technical details on AI-powered feed optimisation, Google Shopping requirements, and building automated pipelines.
How AI-Powered Feed Optimisation Works Under the Hood
When you trigger AI optimisation through the API, the system analyses each product's existing attributes — title, description, brand, product type, category, and any custom fields you've provided. The AI doesn't work in a vacuum; it uses your product data as context to generate optimised output that's specific to your catalogue. A running shoe with the attributes 'Nike, Air Max 90, Men's, Black/White, Size 10' will get a title like 'Nike Air Max 90 Men's Running Shoes - Black/White - Size 10 UK' — specific, keyword-rich, and formatted for Google Shopping.
Title optimisation follows Google Shopping best practices: brand first, product type, then the most important differentiating attributes (colour, size, material, model number). The AI avoids common mistakes like keyword stuffing, excessive capitalisation, and promotional language that violates Google's policies. Description optimisation produces clear, detailed copy that highlights product benefits and specifications without being repetitive.
Category mapping uses a multi-signal approach. The system analyses the product title, description, existing category (if any), brand, and product attributes to determine the most specific Google taxonomy category. A product titled 'Organic Cotton Baby Bodysuit' would be mapped to 'Apparel & Accessories > Clothing > Baby & Toddler Clothing > Baby One-Pieces' rather than the overly broad 'Apparel & Accessories'. This specificity matters — Google rewards products with accurate, granular categories with better ad placement.
- Title optimisation: brand-first format with key attributes, following Google Shopping best practices
- Description enhancement: clear, detailed product copy that avoids policy violations
- Category mapping: multi-signal analysis assigning the most specific Google taxonomy category
- Attribute enrichment: fills in missing fields like product type, colour, material from existing data
- Custom instructions: control optimisation style per feed — e.g. 'always include country of origin'
- Non-destructive: originals preserved so you can compare before/after and revert if needed
Google Shopping Feed Requirements and How the API Handles Them
Google Merchant Centre has strict requirements for product data feeds. Required attributes include id, title, description, link, image_link, price, availability, brand, and condition. For apparel, you also need colour, size, age_group, and gender. Missing any required attribute causes a product to be disapproved — it simply won't appear in Google Shopping results. Beyond required fields, Google also has content policies: no promotional text in titles, no excessive capitalisation, no misleading descriptions.
The AI Shopping Feeds API handles these requirements automatically during export. When you generate a Google Shopping export, the system validates every product against Google's requirements and flags any that would fail. Missing GTINs, incorrect category codes, incomplete size information — all caught before the feed reaches Merchant Centre. The feed audit endpoint lets you check compliance proactively, so you can fix issues before scheduling an export.
Format compliance goes beyond just having the right fields. Google expects specific formats for prices (with currency code), availability values (in_stock, out_of_stock, preorder), and condition values (new, refurbished, used). The API's export engine handles all of this formatting automatically, translating your product data into the exact format Google expects. This means fewer disapprovals, faster feed approval, and more of your products showing up in Shopping results.
Building Automated Feed Pipelines with the REST API
The most common integration pattern is a scheduled pipeline that runs daily: import fresh product data, run AI optimisation on new or changed products, audit the feed for issues, and export to target channels. This can be built with any language that supports HTTP requests — Python, Node.js, Go, Ruby, or even a simple cron job with curl commands.
A basic pipeline looks like this: First, call POST /feeds to import your product data from a source URL (your e-commerce platform's feed export). The API handles parsing and validation. Second, call POST /feeds/{id}/optimise with the fields you want enhanced — typically title, description, and google_category. Third, call GET /feeds/{id}/audit to check for any issues. Fourth, call POST /feeds/{id}/export to generate a compliant feed URL for Google Merchant Centre. That URL can be submitted directly to Merchant Centre as your feed source.
For more sophisticated setups, you can use webhooks from your e-commerce platform to trigger imports whenever products change, rather than running on a fixed schedule. The API also supports partial updates — if only 50 products changed, you can update just those 50 rather than re-importing the entire catalogue. This keeps your feed fresh in near-real-time while minimising API calls.
- Daily pipeline: import, optimise, audit, export — four API calls for a complete feed refresh
- Event-driven: trigger imports from e-commerce platform webhooks for near-real-time updates
- Partial updates: update only changed products instead of re-importing the full catalogue
- Multi-channel: generate exports for Google, Meta, Amazon, and others from the same source data
- Error handling: the audit endpoint catches issues before they reach Merchant Centre
Frequently Asked Questions
Find answers to common questions below.
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