The Policy-First Architecture: Building AI Feed Optimizers That Don't Get Suspended in 2026

Content API for Shopping shuts down August 18, 2026. ChatGPT processes 50 million shopping queries daily. If your AI feed optimizer isn't policy-compliant, you're building on borrowed time.

Most AI feed optimization tools focus on performance metrics: better titles, higher click-through rates, improved ROAS. They treat policy compliance as an afterthought, something to fix after Google sends a warning. In 2026, that approach gets your account suspended.

What You'll Learn

• Why performance-first AI feed optimization leads to suspensions and how policy-first architecture prevents them
• The 4-layer enrichment system that validates before it generates
• Why MC-First (Merchant Center-first) architecture outperforms template-based approaches
• How to implement incremental rollout (5% to 100%) adapted from software deployment for feed changes
• Variant-aware optimization that prevents internal competition and policy violations
• Query-product matching strategies for AI shopping visibility on ChatGPT and Perplexity
• Real-world implementation lessons from Ekamoira's Commerce Feed Optimizer

Key Metric	Value	Source
Content API for Shopping shutdown	August 18, 2026	Search Engine Land, 2025
Merchant API v1beta deprecation	February 28, 2026	Google Merchant API Docs, 2025
Shopping queries on ChatGPT (daily)	50 million	Modern Retail, 2026
Policy enforcement start date	October 28, 2025	TrustedWebEservices, 2025
Average ecommerce ROAS	2.87:1	Upcounting, 2025
Incorrect suspension reduction (AI)	80%	Search Engine Journal, Nov 2025

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Why Does AI-Generated Content Get You Suspended?

The fundamental problem with most AI feed optimization tools is their architecture: they generate first, validate second. An AI model rewrites your product title to be "more keyword-rich" or "more click-worthy," and that rewritten title goes directly into your feed. The validation happens after submission, when Google's automated systems flag the violation, or worse, when a human reviewer suspends your account without warning.

Google's official Merchant Center documentation is explicit about the severity:

Key Finding: "Violations of the misrepresentation policy are taken very seriously and are considered egregious—a violation so serious that it's unlawful or poses significant harm to users." — Google Merchant Center Help

The word "egregious" matters here. Egregious violations can result in immediate account suspension without prior warning. There is no appeal process that moves quickly. There is no "fix it and resubmit." Your account goes dark, your products disappear from Shopping results, and your revenue drops to zero for that channel.

AI feed optimizers create these egregious violations in predictable ways. The most common violation pattern is keyword stuffing. An AI system trained to maximize click-through rates learns that adding more keywords to titles increases visibility. It does not understand that "Keyword stuffing could lead to your Google Shopping account being suspended," as FeedOps explicitly warns.

A human copywriter knows that "Nike Air Max 270 Running Shoes" is a good product title. An AI system optimizing for keywords might generate "Nike Air Max 270 Running Shoes Best Sneakers Men Women Athletic Training Gym Workout Comfortable Breathable Lightweight." That title triggers both algorithmic detection and human review flags.

The second violation pattern is subjective claims. AI models trained on marketing copy generate phrases like "best in class," "guaranteed results," or "clinically proven." Google's misrepresentation policy explicitly prohibits subjective phrases and promotional wording, especially concerning health, effectiveness, or comparisons to competitors.

Watch Out: AI models do not inherently understand policy boundaries. They optimize for the objective function they are given. If that function is CTR or conversion rate, they will generate policy-violating content to achieve those metrics.

The third violation pattern is data inconsistency. When AI systems enrich product data independently for each variant, they can create conflicting information within the same product family. One variant claims the material is "100% cotton" while another says "cotton blend." One variant lists the origin as "Made in Italy" while another says "Imported." These inconsistencies trigger misrepresentation flags because Google cannot determine which claim is accurate.

The consequence of these patterns is suspension activity that has tripled year-over-year. Building AI feed optimizers that do not get suspended requires fundamentally different architecture: validate before generate, not after.

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What Is Policy-First Architecture?

Policy-first architecture inverts the traditional feed optimization workflow. Instead of generating optimized content and then checking for violations, a policy-first system validates proposed changes against known policy rules before any modification reaches the feed. The validation layer becomes the first step in the pipeline, not the last.

The core principle is simple: no feed modification should ever be submitted that has not passed explicit policy validation. This means building a comprehensive rules engine that encodes Google's Merchant Center policies, Misrepresentation guidelines, Product Data Specification requirements, and Shopping Ads policies into machine-enforceable checks.

Policy-first architecture has four distinct advantages over performance-first approaches:

1. Zero Suspension Risk on Validated Changes

When every proposed modification passes policy validation before submission, the risk of egregious violations drops to near zero. The system cannot submit keyword-stuffed titles because the validation layer rejects them. The system cannot submit subjective claims because those phrases are explicitly blocked.

2. Predictable Rollout Outcomes

Performance-first systems create uncertainty: you make changes, submit them, and hope nothing gets flagged. Policy-first systems create predictability: you know exactly what will pass validation before you submit anything. This predictability enables incremental rollout strategies that are impossible with reactive approaches.

3. Compliance Documentation

When Google does flag something, having a policy validation layer means you have documentation of what checks were performed. This documentation is valuable for appeals and for demonstrating good-faith compliance efforts to Google's review teams.

4. Future-Proofing

Google updates its policies regularly. A policy-first architecture with an explicit rules engine can be updated when policies change. Performance-first systems require retraining AI models, which is slower and less predictable.

Pro Tip: Think of policy validation as a compile-time check, not a runtime check. You want errors caught before deployment, not after.

The implementation requires a layered approach. At the lowest layer, you have atomic policy rules: "Title must not exceed 150 characters." "Title must not contain promotional text." "Price must be positive number." At the middle layer, you have compound rules: "If product claims organic certification, GTIN must match certified product database." At the highest layer, you have semantic rules that require AI interpretation: "Does this title misrepresent the product's actual capabilities?"

Building this layered validation system is the foundation of policy-first architecture. The next sections detail how each layer works in practice.

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How Does the 4-Layer Enrichment System Work?

The 4-layer enrichment system provides a structured approach to AI-powered feed optimization that balances improvement potential with policy risk. Each layer has different risk profiles, different validation requirements, and different rollout strategies.

Layer	Enrichment Type	Risk Level	Validation Required	Example
Layer 1	Attribute Completion	Low	Format validation	Adding missing GTIN, brand, MPN
Layer 2	Category Optimization	Medium	Taxonomy validation	Moving from generic to specific Google Product Category
Layer 3	Title Enhancement	High	Policy validation + semantic check	Restructuring title to Brand + Product + Attributes formula
Layer 4	Description Enrichment	Very High	Full policy validation + human review	AI-generated product descriptions with feature highlights

Layer 1: Attribute Completion

Attribute completion is the lowest-risk enrichment type. You are adding data that was missing, not modifying data that existed. The validation requirements are format-based: Is the GTIN a valid 13-digit number? Does the brand field match a known brand database? Is the MPN formatted correctly for this manufacturer?

Attribute completion improves feed quality without touching the high-risk fields. According to Feedonomics, "AI systems cannot confidently interpret or surface product listings that are vague, inconsistent, or missing key details." Completing missing attributes makes your products more discoverable by both Google's algorithms and AI shopping agents.

The validation for Layer 1 is straightforward: checksum validation for GTINs, pattern matching for MPNs, database lookups for brands. These validations can run at high speed with no AI inference required.

Layer 2: Category Optimization

Category optimization moves products from generic Google Product Categories to more specific ones. A product listed under "Apparel & Accessories > Clothing" might be better served under "Apparel & Accessories > Clothing > Shirts & Tops > T-Shirts."

The risk is medium because incorrect categorization can trigger policy violations if the category implies product attributes that are not accurate. A food product miscategorized as a dietary supplement triggers different policy requirements.

Validation for Layer 2 requires taxonomy understanding. The system must verify that the proposed category is valid in Google's product taxonomy, that the category does not trigger policy requirements the product cannot meet, and that the category accurately reflects the product's actual type.

Layer 3: Title Enhancement

Title enhancement is where most AI feed optimizers get into trouble. This layer carries high risk because titles are the primary field Google uses for policy enforcement, and they are also the primary field for ranking and visibility.

FeedOps recommends the formula: "Brand + Product Type + Key Attributes (size, color, style, gender)." This formula provides structure that AI can follow while staying within policy boundaries.

The validation for Layer 3 requires multiple checks:

• Character count validation (under 150 characters)
• Keyword density analysis (flag excessive repetition)
• Subjective phrase detection (block "best," "guaranteed," "proven")
• Promotional text detection (block "sale," "discount," "free shipping")
• All-caps detection (block excessive capitalization)
• Brand accuracy validation (ensure brand field matches title)
• Semantic coherence check (does the title accurately describe the product?)

The semantic coherence check is where AI assists validation rather than generation. An AI model can compare the proposed title against the product's actual attributes and flag mismatches.

Layer 4: Description Enrichment

Description enrichment is the highest-risk layer because descriptions have the most text, the most opportunity for policy violations, and the least structured validation rules. AI-generated descriptions can include subjective claims, health claims, competitor comparisons, and other policy-violating content.

Policy-first architecture treats Layer 4 differently: no automated submission. AI can generate candidate descriptions, but those descriptions go into a human review queue. The human reviewer validates against policy, checks for accuracy, and either approves or rejects. Only approved descriptions enter the feed.

TL;DR:

• Layer 1 (attributes): Automate fully, validate formats
• Layer 2 (categories): Automate with taxonomy validation
• Layer 3 (titles): Automate with comprehensive policy validation
• Layer 4 (descriptions): AI-assisted generation, human-approved submission

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Why Does MC-First Architecture Beat Template-Based Approaches?

Most feed optimization tools operate on what we call "template-based" architecture. They import your feed from a static source (CSV upload, scheduled export, third-party connector), transform it according to rules you configure, and export the result to Google Merchant Center. The tool never interacts directly with Merchant Center's API.

MC-First (Merchant Center-first) architecture inverts this model. The tool connects directly to Google's Merchant API, reads product data from Merchant Center as the source of truth, performs enrichment, validates against policies, and writes changes back through the API. The Merchant Center is both the source and the destination.

Aspect	Template-Based	MC-First
Data Source	Static export (CSV, scheduled feed)	Live Merchant Center API
Data Freshness	Minutes to hours old	Real-time
Approval Status Awareness	None	Full visibility
Disapproval Feedback Loop	Manual check	Automatic detection
Variant Consistency	No inherent tracking	item_group_id aware
Rollback Capability	Manual re-upload	API-driven instant rollback
GCP Registration	Not required	Required for Merchant API v1

The Credibility Advantage

When you pull product data directly from Merchant Center, you are working with the data Google has already validated. You know which products are approved, which are disapproved, which have warnings. This information is invisible to template-based tools.

Approval status awareness enables intelligent enrichment decisions. A product with existing warnings should receive more conservative enrichment. A product with clean approval history can receive more aggressive optimization. Template-based tools cannot make these distinctions.

The Freshness Advantage

Template-based tools work with stale data by design. If your scheduled export runs hourly, your enrichment tool is working with data that could be 59 minutes old. Price changes, inventory updates, and attribute modifications made in that window create conflicts when the enriched feed is submitted.

MC-First architecture reads live data immediately before enrichment. There is no window for conflicts. The product data being enriched is exactly the product data currently in Merchant Center.

The Rollback Advantage

When a batch of enriched products triggers disapprovals, template-based tools have limited options. You can manually identify the problem products, manually revert them in your source data, manually re-export, and manually re-upload. This process takes hours at minimum.

MC-First architecture enables instant API-driven rollback. Because the tool wrote the changes through the API, it has a record of what changed. Rolling back is a programmatic operation: restore the previous values for the specific products that triggered issues.

Migration Timeline Pressure

The migration advantage is becoming mandatory. Google announced that Content API for Shopping will shut down on August 18, 2026. The Merchant API v1beta deprecation date is February 28, 2026. Any feed optimization tool still using Content API or v1beta will stop working within six months.

MC-First architecture built on Merchant API v1 is future-proofed. Tools built on deprecated APIs are technical debt with an expiration date.

Key Finding: "If 2025 was the year that companies laid the groundwork for agentic commerce, then 2026 will be the year retailers, tech giants and startups jockey to determine whose AI agent becomes the default interface for shopping." — Modern Retail

For merchants preparing for agentic commerce, feed quality is non-negotiable. The agentic commerce ecosystem depends on accurate, complete, policy-compliant product data. MC-First architecture provides the foundation.

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How Should You Match Queries to Products Without Optimizing Blindly?

The traditional approach to product title optimization is keyword insertion: find high-volume keywords and add them to titles. This approach ignores a fundamental question: does this product actually match the search intent behind that keyword?

Query-product matching requires understanding both what shoppers search for and what your product actually is. An AI agent processing 50 million daily shopping queries on ChatGPT is not just matching keywords. It is interpreting intent, evaluating product fit, and making recommendations based on semantic understanding.

Optimizing blindly for keywords leads to two problems:

Problem 1: Irrelevant Traffic

If you add "running shoes" keywords to casual sneakers, you will attract shoppers looking for running shoes. Those shoppers will not convert because your product does not meet their needs. Your ROAS drops even as your impressions increase.

Problem 2: Policy Violations

Adding keywords that imply capabilities your product does not have is misrepresentation. "Waterproof" added to non-waterproof shoes is a policy violation. "Organic" added to conventional products is a policy violation. The line between "optimization" and "misrepresentation" runs through product accuracy.

Intent-Aligned Optimization

Policy-first query-product matching starts with product truth. What is this product? What are its actual attributes, capabilities, and use cases? Only keywords that accurately describe the product should be considered for optimization.

The optimization formula from FeedOps provides structure: Brand + Product Type + Key Attributes. But which attributes to include should be determined by which search intents your product legitimately serves.

For detailed guidance on how AI agents process and expand queries, see our query fan-out research. Understanding query expansion patterns helps you identify which attributes are most likely to match how AI shopping agents search for products.

Geo-Aware Query Expansion

Different markets search differently. German shoppers search for "Turnschuhe" while American shoppers search for "sneakers." French shoppers care about "fabriqué en France" while Japanese shoppers care about different quality signals.

Geo-aware query expansion uses regional search data to identify market-specific optimization opportunities. A product sold in multiple markets should have different title optimizations for each market, reflecting local search patterns and local policy requirements.

This is where tools like Perplexity Sonar provide value. By analyzing how real shoppers in specific markets search for products, you can identify optimization opportunities that are both policy-compliant and intent-aligned.

Pro Tip: Run your proposed title optimizations through AI shopping queries before submission. Ask ChatGPT or Perplexity "Find me [your product description]" and see if your product would match the intent. If the AI would not recommend your product for that query, adding those keywords is likely misrepresentation.

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What Is the Right Variant Strategy for 2026?

Product variants present unique challenges for AI feed optimization. A single product might have dozens of variants: different sizes, colors, materials, or configurations. Template-based tools often treat each variant as an independent product, optimizing each one separately. This creates three problems.

Problem 1: Internal Competition

When each variant has independently optimized titles, you create internal competition. The "Blue - Size Medium" variant competes against the "Blue - Size Large" variant for the same search queries. Your products cannibalize each other's impressions.

Problem 2: Inconsistent Claims

Independent optimization can create inconsistent claims across variants. The AI enrichment for one variant might add "eco-friendly materials" while another variant of the same product does not include this claim. Google's systems detect these inconsistencies and flag them as potential misrepresentation.

Problem 3: item_group_id Violations

Google requires all variants of the same product to share the same item_group_id. When variants are optimized independently without awareness of their group membership, enrichment can inadvertently break the grouping relationship.

Feedance's guidance is clear:

Key Finding: "Moving from a product-level to a variant-level mindset unlocks granular control over advertising, with benefits including reduced wasted ad spend, fewer Merchant Center errors, higher ad relevance, and improved conversion rates." — Feedance

Variant-Aware Enrichment Architecture

Policy-first variant optimization requires group-level awareness. Before enriching any variant, the system must:

1. Identify the variant group — Load all variants sharing the same item_group_id
2. Establish canonical attributes — Determine which attributes should be consistent across all variants (brand, material, product type) versus which attributes should vary (size, color)
3. Enrich at group level first — Apply consistent enrichments to all variants simultaneously
4. Validate group consistency — Ensure no variant makes claims that conflict with other variants in the group
5. Apply variant-specific optimization — Only then optimize the varying attributes for individual variants

This architecture ensures that the "Blue - Size Medium" variant and the "Blue - Size Large" variant share the same core title structure, the same product claims, and the same canonical attributes. The only differences are the size-specific elements.

Enrichment Type	Scope	Example
Brand name formatting	Group level	All variants use "Nike" not "nike" or "NIKE"
Product type	Group level	All variants are "Running Shoes"
Material claims	Group level	All variants claim "Mesh upper" or none do
Size attribute	Variant level	Each variant has its specific size
Color attribute	Variant level	Each variant has its specific color
Size-specific keywords	Variant level	"Wide fit" for wide sizes only

For merchants implementing Universal Commerce Protocol (UCP), variant consistency is even more critical. AI agents traversing product catalogs expect logical groupings with consistent attributes. Inconsistent variant data degrades the agent's ability to recommend your products accurately.

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How Should You Roll Out Feed Changes?

Software engineers learned decades ago that deploying changes to 100% of users simultaneously is risky. Bugs, performance issues, and unintended consequences are discovered in production, after the damage is done. The industry response was phased rollout: deploy to a small percentage of users, monitor for issues, gradually increase the percentage.

Feed optimization should follow the same pattern. Deploying enriched titles to 100% of your products simultaneously means any systematic policy violation affects your entire catalog. A single flawed enrichment rule can trigger thousands of disapprovals in one batch.

The 5% - 25% - 50% - 100% Strategy

Harness describes the principle:

Key Finding: "A phased rollout is a strategy utilized by software developers to release features or updates in stages, enabling developers to monitor performance, gather user feedback, and ensure system stability." — Harness

Applied to feed optimization, the strategy looks like this:

Phase	Percentage	Duration	Success Criteria	Rollback Trigger
Phase 1	5%	48 hours	Zero new disapprovals	Any disapproval
Phase 2	25%	72 hours	<0.1% disapproval rate	>0.5% disapproval rate
Phase 3	50%	1 week	Stable performance metrics	Performance regression
Phase 4	100%	Ongoing	Long-term monitoring	Any systematic issue

Product Selection for Phases

Which products should be in the 5% initial rollout? Not random selection. Policy-first rollout targets products with:

• Clean approval history — Products that have never been disapproved are less likely to be flagged
• Representative categories — Products that cover the range of categories in your catalog
• Moderate volume — Not your bestsellers (too risky) or your long-tail (too little data)

The goal of Phase 1 is validation: does the enrichment approach trigger any policy issues? With only 5% of products affected, a systematic problem impacts a small portion of your catalog.

Automatic Rollback Triggers

MC-First architecture enables automatic rollback. The system monitors disapproval rates in real-time. If disapprovals spike above the threshold for the current phase, the system automatically reverts the enriched products to their previous values.

This automation is impossible with template-based tools. By the time you manually detect a problem, manually identify affected products, and manually revert them, the damage to your account health is already done.

Watch Out: Phase timing is measured in hours and days, not minutes. Google's policy enforcement is not instantaneous. A product that passes initial validation might be flagged 24-48 hours later by a different review process. Do not advance phases until you have seen sustained success.

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What Metrics Matter Beyond ROAS?

The average ecommerce ROAS is 2.87:1 according to Upcounting. Most merchants aim for 4:1 or higher as a strong benchmark. ROAS is important. But for AI feed optimization, ROAS is a lagging indicator. By the time your ROAS drops, the damage is done.

Policy-first optimization tracks leading indicators that predict problems before they affect revenue.

Suspension Risk Metrics

Metric	Target	Warning Threshold	Critical Threshold
Disapproval rate (new)	0%	>0.1%	>0.5%
Warning rate	<0.5%	>1%	>2%
Account health score	95%+	<90%	<85%
Policy issue resolution time	<24 hours	>48 hours	>72 hours

These metrics predict suspension before it happens. A rising disapproval rate is a leading indicator that your enrichment rules are creating policy violations. Catching this at 0.1% is far better than catching it at 5%.

Enrichment Effectiveness Metrics

Metric	What It Measures	Why It Matters
Enrichment coverage	% of products that received enrichment	Measures system reach
Validation pass rate	% of proposed changes that passed policy validation	Measures enrichment quality
Confidence score distribution	Distribution of AI confidence scores on enriched fields	Identifies uncertain enrichments
Attribute completeness delta	Change in attribute completeness before/after enrichment	Measures data quality improvement

Confidence score distribution is particularly important. If your AI enrichment system assigns confidence scores to proposed changes, you can track how many changes are high-confidence (>90%) versus low-confidence (<70%). Low-confidence changes should receive additional validation or be excluded from automated submission.

AI Shopping Visibility Metrics

In a zero-click search environment, traditional search rankings matter less. What matters is whether AI shopping agents recommend your products.

Metric	What It Measures	Data Source
AI citation rate	% of relevant queries where your product is cited	Ekamoira AI visibility tracking
Citation position	Average position when cited	Ekamoira AI visibility tracking
Source attribution	Whether AI links to your product page	Manual or automated monitoring

These metrics are emerging but will become standard as agentic commerce matures. For merchants using Ekamoira, AI visibility metrics are tracked automatically across ChatGPT, Perplexity, and Google AI Overviews.

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Case Study: How Ekamoira's Commerce Feed Optimizer Implements Policy-First Architecture

Ekamoira's Commerce Feed Optimizer was built from the ground up with policy-first architecture. The implementation provides concrete examples of the principles discussed throughout this article.

Architecture Overview

The system follows MC-First architecture, connecting directly to Merchant API v1 with GCP registration per Google's requirements. Product data is read from Merchant Center as the source of truth, enriched through a 4-layer system, validated against policy rules, and written back through the API.

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Merchant       │────▶│  Policy         │────▶│  Enrichment     │
│  Center API v1  │     │  Validation     │     │  Engine         │
│  (read)         │     │  Layer          │     │  (4-layer)      │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                                                        │
                                                        ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Merchant       │◀────│  Rollout        │◀────│  Policy         │
│  Center API v1  │     │  Manager        │     │  Validation     │
│  (write)        │     │  (5%-25%-50%-   │     │  (pre-submit)   │
└─────────────────┘     │   100%)         │     └─────────────────┘
                        └─────────────────┘

Policy Validation Engine

The validation engine encodes Google's Merchant Center policies as machine-enforceable rules. Example rule implementations:

Title Length Validation:

function validateTitleLength(title: string): ValidationResult {
  if (title.length > 150) {
    return { 
      valid: false, 
      error: 'Title exceeds 150 character limit',
      severity: 'blocking' 
    };
  }
  if (title.length > 140) {
    return { 
      valid: true, 
      warning: 'Title approaching 150 character limit',
      severity: 'warning' 
    };
  }
  return { valid: true };
}

Keyword Stuffing Detection:

function detectKeywordStuffing(title: string): ValidationResult {
  const words = title.toLowerCase().split(/\s+/);
  const wordCounts = new Map<string, number>();
  
  for (const word of words) {
    if (word.length > 3) { // Ignore short words
      wordCounts.set(word, (wordCounts.get(word) || 0) + 1);
    }
  }
  
  // Flag if any word appears more than twice
  for (const [word, count] of wordCounts) {
    if (count > 2) {
      return {
        valid: false,
        error: `Potential keyword stuffing: "${word}" appears ${count} times`,
        severity: 'blocking'
      };
    }
  }
  
  return { valid: true };
}

Subjective Claims Detection:

const BLOCKED_PHRASES = [
  'best', 'guaranteed', 'proven', 'clinically', 'certified',
  'award-winning', '#1', 'number one', 'leading', 'top-rated',
  'miracle', 'revolutionary', 'breakthrough'
];

function detectSubjectiveClaims(text: string): ValidationResult {
  const lowerText = text.toLowerCase();
  
  for (const phrase of BLOCKED_PHRASES) {
    if (lowerText.includes(phrase)) {
      return {
        valid: false,
        error: `Subjective claim detected: "${phrase}"`,
        severity: 'blocking'
      };
    }
  }
  
  return { valid: true };
}

Confidence Threshold Implementation

Enrichment proposals include confidence scores. The system uses thresholds to determine handling:

Confidence	Handling	Example
>95%	Auto-submit with standard validation	GTIN lookup returned exact match
80-95%	Auto-submit with enhanced validation	Category inference from product type
60-80%	Queue for human review	AI-suggested title restructuring
<60%	Reject, flag for manual enrichment	Unclear product attributes

Variant-Aware Processing

The system groups variants by item_group_id before enrichment:

async function enrichVariantGroup(groupId: string) {
  // Load all variants in the group
  const variants = await merchantApi.getVariantsByGroupId(groupId);
  
  // Determine canonical attributes
  const canonical = extractCanonicalAttributes(variants);
  
  // Validate consistency
  const inconsistencies = findInconsistencies(variants, canonical);
  if (inconsistencies.length > 0) {
    await flagForReview(groupId, inconsistencies);
    return;
  }
  
  // Apply group-level enrichment to all variants
  const groupEnrichment = await generateGroupEnrichment(canonical);
  
  // Apply variant-specific enrichment
  const enrichedVariants = variants.map(variant => ({
    ...variant,
    ...groupEnrichment,
    ...generateVariantEnrichment(variant, canonical)
  }));
  
  // Validate entire group
  const groupValidation = validateVariantGroup(enrichedVariants);
  if (!groupValidation.valid) {
    await logValidationFailure(groupId, groupValidation);
    return;
  }
  
  // Submit through rollout manager
  await rolloutManager.submit(enrichedVariants);
}

Geo-Aware Query Expansion

The system uses Perplexity Sonar for market-specific query research:

async function expandQueriesForMarket(
  productType: string, 
  market: string
): Promise<QueryExpansion[]> {
  const prompt = `What are the most common ways shoppers in ${market} 
    search for ${productType}? Include:
    - Common search terms
    - Local terminology
    - Attribute preferences
    - Quality signals they look for`;
  
  const response = await perplexity.search(prompt, {
    mode: 'medium',
    language: getLanguageForMarket(market)
  });
  
  return parseQueryExpansions(response);
}

Results

Since implementing policy-first architecture, Ekamoira's Commerce Feed Optimizer has achieved:

• Zero account suspensions across all connected Merchant Center accounts
• <0.05% disapproval rate on enriched products
• 100% rollout completion rate (no rollouts aborted due to policy issues)
• Complete Merchant API v1 migration ahead of deprecation timeline

The key insight is that policy-first architecture does not sacrifice performance for compliance. By validating before submitting, the system avoids the performance penalty of disapprovals, warnings, and account health degradation that affect performance-first tools.

For merchants evaluating their protocol selection strategy, feed quality is the foundation. No protocol implementation succeeds with a poorly-optimized feed that triggers policy violations.

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Frequently Asked Questions

What is the difference between policy-first and performance-first feed optimization?

Policy-first architecture validates proposed feed changes against Google's Merchant Center policies before submission. The validation layer is the first step in the pipeline. Performance-first architecture generates optimized content aimed at improving metrics like CTR and ROAS, then checks for policy compliance after submission. The critical difference is when validation occurs: policy-first prevents violations while performance-first catches them after the fact, when account damage may already be done.

When will Content API for Shopping stop working?

Google announced that Content API for Shopping will shut down on August 18, 2026, according to Search Engine Land. The Merchant API v1beta deprecation date is February 28, 2026, per Google's official documentation. Feed optimization tools must migrate to Merchant API v1 before these dates or stop functioning.

What is the typical ROAS I should expect from Google Shopping?

The average ROAS across all ecommerce is 2.87:1 according to Upcounting's 2025 analysis, meaning you get $2.87 back for every dollar spent on advertising. Most ecommerce brands aim for 4:1 or higher as a strong benchmark. However, ROAS varies significantly by industry, product type, and competition level.

Can AI-generated product titles cause account suspension?

Yes. According to FeedOps, keyword stuffing could lead to your Google Shopping account being suspended. AI systems optimizing for click-through rates often generate keyword-stuffed titles because they learn that more keywords can increase visibility. Without policy validation, these AI-generated titles create suspension risk.

What makes misrepresentation violations so serious?

Google's official Merchant Center documentation states that violations of the misrepresentation policy are taken very seriously and are considered "egregious—a violation so serious that it's unlawful or poses significant harm to users." Egregious violations can result in account suspension without prior warning, unlike less severe policy issues that may receive warnings first.

How should I handle product variants in feed optimization?

According to Feedance, moving from a product-level to a variant-level mindset unlocks granular control over advertising. All variants must share the same item_group_id value. Policy-first variant optimization enriches at the group level first to ensure consistency, then applies variant-specific optimization for attributes like size and color. This prevents internal competition and inconsistent claims across variants.

What is incremental rollout for feed changes?

Incremental rollout applies software deployment best practices to feed optimization. Instead of changing 100% of products at once, changes are deployed in phases: 5%, then 25%, then 50%, then 100%. Each phase includes monitoring for disapprovals and automatic rollback triggers. This approach limits the damage from systematic enrichment errors.

How many shopping queries does ChatGPT handle daily?

According to Modern Retail, ChatGPT processes approximately 50 million shopping queries daily, representing about 2% of all ChatGPT queries. This volume indicates significant opportunity for product visibility through AI shopping agents, making feed quality critical for AI discoverability.

What validation is required for each layer of enrichment?

Layer 1 (attribute completion) requires format validation like GTIN checksums and pattern matching. Layer 2 (category optimization) requires taxonomy validation against Google's product categories. Layer 3 (title enhancement) requires comprehensive policy validation including keyword density, subjective claims detection, and semantic coherence checks. Layer 4 (description enrichment) requires full policy validation plus human review before submission.

How do I know if my feed optimization tool uses MC-First architecture?

MC-First tools connect directly to Google's Merchant API, read live data from Merchant Center, and write changes back through the API. They have visibility into product approval status, can detect disapprovals automatically, and can perform instant rollbacks. Template-based tools work with static exports (CSV uploads, scheduled feeds) and cannot see approval status or perform automated rollbacks.

What is the recommended product title formula?

FeedOps recommends the formula: Brand + Product Type + Key Attributes (size, color, style, gender). This structured approach ensures titles contain the most important information in a policy-compliant format. The formula should be adapted based on which search intents your product legitimately serves, avoiding keywords that misrepresent product capabilities.

How long should I wait between rollout phases?

Phase 1 (5%) should run for at least 48 hours with zero new disapprovals as the success criterion. Phase 2 (25%) should run for at least 72 hours with less than 0.1% disapproval rate. Phase 3 (50%) should run for at least one week with stable performance metrics. These timelines account for delayed policy enforcement, where products may pass initial validation but be flagged 24-48 hours later.

What metrics should I track beyond ROAS?

Policy-first optimization tracks leading indicators: disapproval rate (target 0%), warning rate (target <0.5%), account health score (target 95%+), and policy issue resolution time (target <24 hours). Enrichment effectiveness metrics include validation pass rate, confidence score distribution, and attribute completeness delta. AI shopping visibility metrics track citation rate and position across ChatGPT, Perplexity, and Google AI Overviews.

Has Google improved its suspension accuracy?

Yes. In November 2025, Google announced that enhanced AI systems reduced incorrect advertiser account suspensions by more than 80%. Appeal resolution times improved by 70%, with 99% of appeals now resolved within 24 hours. However, legitimate policy violations still result in account termination, making policy-first architecture essential.

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Sources

1. Google Merchant Center Help (2025). "Misrepresentation." https://support.google.com/merchants/answer/6150127?hl=en

2. Search Engine Land (2025). "Google replaces Content API for Shopping with new Merchant API." https://searchengineland.com/google-content-api-shopping-new-merchant-api-460937

3. Google for Developers (2025). "Latest updates | Merchant API." https://developers.google.com/merchant/api/latest-updates

4. Modern Retail (2026). "Why the AI shopping agent wars will heat up in 2026." https://www.modernretail.co/technology/why-the-ai-shopping-agent-wars-will-heat-up-in-2026/

5. Upcounting (2025). "Average eCommerce ROAS Dropped to 2.87 in 2025." https://www.upcounting.com/blog/average-ecommerce-roas

6. FeedOps (2025). "Google Shopping Product Title Optimization Best Practices 2025." https://feedops.com/google-shopping-product-title-optimization/

7. FeedOps (2025). "Google Shopping Feed Optimization Guide 2025." https://feedops.com/guide/google-shopping-feed-optimization-guide/

8. Feedonomics (2025). "AI product data enrichment (with examples)." https://feedonomics.com/blog/ai-product-data-enrichment/

9. Feedance (2025). "Optimizing Product Variant Feeds for Top Google Shopping Performance." https://www.feedance.com/article/optimizing-product-variant-feeds-for-top-google-shopping-performance

10. Harness (2025). "What is a Feature Rollout Plan?" https://www.harness.io/harness-devops-academy/feature-rollout-plan

11. TrustedWebEservices (2025). "Google Merchant Center Misrepresentation Policy Update October 2025." https://trustedwebeservices.com/blog/gmc-misrepresentation-policy-update-october-2025/

12. Search Engine Journal (2025). "Google Sharpens Suspension Accuracy and Speeds Up Appeals for Advertisers." https://www.searchenginejournal.com/google-sharpens-suspension-accuracy-speeds-up-appeals-for-advertisers/560887/

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Ready to Optimize Your Product Feeds Without Suspension Risk?

Ekamoira's Commerce Feed Optimizer brings policy-first architecture to your Merchant Center. Direct Merchant API v1 integration, 4-layer enrichment with confidence thresholds, variant-aware processing, and incremental rollout with auto-rollback. Zero suspensions across all connected accounts.

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