The coherent catalog: how Newtone's AI infrastructure addresses the enterprise discoverability challenge

Executive summary

Enterprise catalogs face a fundamental discoverability challenge across every channel. While retailers focus on creating more content, the underlying issue isn't volume - it's data quality and consistency. Most enterprise catalogs are structurally insufficient for competing in today's fragmented discovery landscape, where products must perform across marketplaces, multi-brand retail platforms, AI-powered search, and owned e-commerce simultaneously.

Conventional enrichment systems extract 5-7 basic attributes per product - typically just Color, Price, Category, Material, and Size. This shallow tagging creates a two-tier catalog where hero products compete whilst 60-80% of inventory remains functionally invisible across discovery channels. The consequences: marketplace rankings suffer, products disappear from filtered retail views, AI systems cannot cite your catalog as authoritative, and on-site conversion stagnates.

Newtone's proprietary multimodal AI takes a fundamentally different approach: intelligent, category-optimized attribute extraction that delivers 20-35+ strategically selected attributes per SKU. This isn't merely about volume - it's about precision. Our research demonstrates that 10-15 distinct color-related attributes represent the optimal range for fashion discovery. Fewer color attributes and products miss critical search queries; more attributes and algorithmic noise degrades performance.

The impact is measurable: 18-34% improvement in marketplace organic rankings, 15-30% increase in multi-brand retail impressions, 3-6x higher AI citation frequency, and 8-15% reduction in returns through superior expectation management. More importantly, these advantages compound across channels, creating structural competitive advantages that competitors using conventional enrichment cannot replicate.

The enterprise retailers succeeding across discovery channels aren't those with larger marketing budgets - they're the ones who recognised that intelligent catalog enrichment represents a strategic imperative and invested in specialized AI infrastructure to build and maintain it.

I. The multi-channel discovery imperative

Your catalog must perform simultaneously across fundamentally different algorithmic environments:

• Marketplaces such as Amazon, Zalando, and Farfetch where discovery depends entirely on structured data matching platform-specific ranking algorithms.

• Multi-Brand Retail environments - department stores and specialty retailers - where customers browse 50+ brands and your products must surface in filtered views powered by the retailer's search infrastructure.

• AI-Mediated Search including ChatGPT Shopping, Google AI Overview, and Perplexity where discovery occurs through natural language queries and AI synthesis.

• Owned E-Commerce channels where filtering, search, and navigation depend on the richness of underlying product data.

The common denominator: all are algorithmic discovery systems that rank and surface products based on data completeness, structure, and relevance signals. Shallow, inconsistent product data doesn't simply hurt performance in one channel - it systematically undermines discoverability across all channels.

II. The enterprise data challenge

Enterprise retailers manage millions of SKUs sourced from dozens of suppliers, stored across legacy PIMs and ERPs, and updated by global teams. This operational complexity creates profound data inconsistency that undermines competitive positioning.

The three critical limitations

Attribute Insufficiency: Hero products might possess 20 detailed attributes whilst long-tail products - 60-80% of inventory - have only 5-7 basic tags. On marketplaces, this relegates long-tail offerings to obscurity. In multi-brand retail, inconsistent depth means products randomly disappear from filtered category views.

Structural Inconsistency: Different teams employ different terminology for identical concepts. One team tags "Jacket Fit" whilst another uses "Garment Silhouette." Product managers in France structure data differently from those in Japan. This fragmentation prevents reliable feed mapping and creates data quality indicators that diminish search rankings.

Categorical Gaps: Essential attributes for one category are entirely absent from similar categories. "Closure Type" exists for outerwear but is missing from swimwear. "Thread Count" appears in bedding but not curtains. Products cannot surface in filtered search, lose to competitors in comparisons, and become impossible for AI systems to cite.

Large portions of your catalog are systematically disadvantaged across every discovery channel. The issue isn't product quality - it's structural invisibility.

III. The Newtone difference: intelligent attribute extraction

The true measure of an AI enrichment platform isn't raw volume - it's whether the system intelligently determines optimal attribute depth for each data category, maintains that standard across every SKU, and formats correctly for every discovery channel.

Beyond simple volume: category-optimized intelligence

Conventional platforms extract a small set of basic tags uniformly. Newtone's multimodal AI recognises that different attribute categories require different levels of granularity:

Color Attributes: Research demonstrates that 10-15 distinct color-related attributes represent the optimal range for maximum discoverability without introducing noise. This isn't arbitrary - it's based on analysing how customers search and what algorithmic systems prioritise.

Material/Texture: 6-8 attributes for basic accessories, 15-20+ for technical apparel where material performance is critical.

Fit & Sizing: Fashion categories benefit from 2-4 distinct fit attributes, whilst home goods require only 1-2 dimensional specifications.

Functional Features: Technical products require 20-30+ attributes; decorative items need fewer but highly descriptive attributes.

Newtone doesn't simply extract more - it extracts intelligently, ensuring each category receives the precise attribute depth required to compete effectively.

Capability vs. optimization

Newtone is capable of extracting far more than the baseline we typically deliver. Our multimodal AI can generate dozens of attributes per category when needed. Through testing across millions of products and thousands of discovery scenarios, we've identified optimal ranges that maximise discoverability without diminishing signal quality.

The 20-35 total attributes baseline represents our strategic optimization, not our technical ceiling. For specific high-value categories, we regularly deliver 40-50+ attributes per SKU when that level of granularity drives measurable performance improvements.

IV. Case study: intelligent color extraction

Color queries dominate fashion and home goods searches. This is where Newtone's category-optimized approach becomes particularly evident - and where conventional enrichment underperforms across every channel.

Generic tagging systems using basic RGB detection return single-word tags: "Blue," "Red," "Green." This creates significant challenges:

On Marketplaces: Customers filtering for "navy blue jumpers" see results including royal blue, sky blue, and midnight black products all tagged "Blue," leading to poor experience and bounce rates that diminish overall search ranking.

In Multi-Brand Retail: When a customer searches for "deep navy knitwear" on a department store site, your navy jumper with generic "Blue" tagging competes against a competitor's product with richer color data. The competitor secures the impression.

In AI Search: ChatGPT cannot cite your product for "a deep indigo jumper with subtle texture" because your data contains only "Blue" and "Jumper."

Newtone's optimized color intelligence: 10-15 strategic attributes

Consider a navy jumper with contrast stitching. Whilst Newtone could generate 30+ color attributes, analysis demonstrates that 10-15 carefully selected attributes represent the optimal range for this category:

Result: 10 strategically selected color attributes versus 1 generic tag. This represents a 10x advantage in the color category alone - and color is just one of 8-12 major attribute categories where Newtone applies similar optimization.

Why 10-15 color attributes represents the optimal range for fashion

Through testing across millions of discovery scenarios:

10-15 color attributes maximise matching opportunities across search queries, filters, and AI synthesis without creating redundancy that dilutes signal quality.

Below 10: Products miss significant discovery opportunities - the difference between page 8 and page 1 visibility.

Above 15-20: Diminishing returns emerge. Additional attributes add marginal value whilst increasing the risk of conflicting signals that actually diminish algorithmic performance in some channels.

Newtone's intelligence: We've mapped these optimal ranges for every major product category and attribute type.

V. Strategic impact across discovery channels

Category-optimized intelligent enrichment creates measurable business outcomes:

Marketplace Performance: Amazon's A9 and similar algorithms explicitly reward data completeness and relevance. Newtone clients achieve 18-34% improvement in organic search ranking within 90 days, 25-40% increase in impression share, and 12-22% reduction in advertising cost-per-acquisition. Marketplace algorithms detect and penalise attribute redundancy - Newtone's category-optimized approach ensures every attribute adds genuine relevance signals.

Multi-Brand Retail Excellence: In multi-brand environments, products lacking any required attribute become invisible in filtered combinations. Newtone ensures 100% of qualifying products surface in all applicable filtered views (versus 60-75% with conventional tagging), delivering 15-30% increase in product impressions and 8-18% improvement in click-through rate.

AI-Mediated Discovery: Research demonstrates the top-cited retailer captures 60-70% of AI-mediated visibility. Newtone clients achieve 3-6x higher citation frequency compared to shallow enrichment, securing 45-67% of category visibility and capturing 25-40% of long-tail query traffic that competitors cannot reach.

Owned Channel Conversion: Comprehensive, consistent filtering drives 15-25% increase in filter usage rates, 8-15% reduction in cart abandonment, and 12-20% improvement in conversion rate for long-tail products.

Returns Reduction: Category-optimized attributes provide expectation-setting details across fit precision (8-12 attributes), tactile inference (10-15 attributes), functional clarity (6-10 attributes), and care complexity (4-6 attributes), delivering 8-15% reduction in returns.

VI. The operational advantage

Category-optimized rich data dramatically reduces operational costs of multi-channel commerce.

Feed mapping and marketplace onboarding

When your catalog consistently contains 20-35+ intelligently selected attributes per SKU, automated feed mapping becomes reliable. We maintain pre-built mappings to 40+ marketplace schemas, and our comprehensive source data populates 95-99% of optional fields that competitors leave blank. New SKUs reach all channels in hours rather than weeks. Marketplace expansion occurs in days rather than months.

Multi-language localisation

Consistent source attributes with 20-35+ optimized data points enable predictable, high-quality translation across 42 languages. Our custom-trained AI maintains >99% brand voice accuracy whilst preserving all structured data relationships, delivering 60-75% reduction in localisation costs per SKU with simultaneous improvement in quality and consistency.

VII. The Newtone infrastructure

Achieving category-optimized attribute depth across millions of SKUs, formatted correctly for dozens of channels, requires purpose-built AI infrastructure.

Four layers of intelligent consistency enforcement

Category-Aware Gap Detection: Newtone doesn't simply identify missing attributes - it understands which attributes matter most for each product category. Our system compares each product against category-specific optimal models, identifies gaps that create discovery disadvantages, prioritises inference based on attribute impact, and automatically triggers multimodal analysis to infer and populate strategic gaps with >99% accuracy.

Brand-Specific Training: Intelligent attribute extraction provides value only when language aligns with your brand. Newtone's custom AI training learns exclusively from your proprietary content, style guides, and terminology, delivering millions of product descriptions that maintain >99% brand voice accuracy whilst providing category-optimized attribute depth.

Channel-Optimized Content Variants: Newtone maintains one enriched source of truth with 20-35+ optimized attributes and automatically generates channel-optimized variants - from Amazon bullets to AI-friendly natural language to multi-brand retail structured feeds to 42 language variants.

Continuous Optimization: When human editors validate or correct an AI-inferred attribute, that correction feeds instantly back into our models. We also track which attributes drive performance across different channels and continuously refine category-specific optimization models.

VIII. Conclusion: intelligence and scale as strategic advantage

Enterprise retailers succeeding across fragmented discovery channels aren't those with the largest marketing budgets - they're the ones who recognised that intelligent, category-optimized catalog enrichment represents a strategic imperative.

Newtone's approach represents a fundamental evolution beyond first-generation AI tagging: not simply more attributes, but strategically optimized depth per category; not merely data extraction, but intelligent inference that recognises 10 -15 color attributes as optimal; not volume at any cost, but precision-engineered enrichment that maximises algorithmic performance; not one-time enrichment, but continuous optimization that adapts as discovery channels evolve.

In an environment where marketplaces reward data completeness, multi-brand retailers prioritise reliable feeds, and AI systems cite only the most authoritative sources, having a catalog enriched by category-optimized AI isn't an advantage - it's the baseline requirement.

The question isn't whether to invest in intelligent enrichment infrastructure - it's whether you'll establish category leadership or accept second-tier visibility across every discovery channel.

The retailers who recognise this strategic imperative early will build their content operations on infrastructure designed for intelligence at scale.

About Newtone: We provide enterprise-grade AI content infrastructure that delivers >99% brand voice accuracy whilst maintaining category-optimized enrichment across your entire catalog. Our custom-trained multimodal AI intelligently extracts 20-35+ strategic attributes per SKU (with capability for 40-50+ when specialized categories demand it), enabling global retailers to establish leadership across marketplaces, multi-brand retail, AI search, and owned channels simultaneously. Learn more at newtone.ai.