Infrastructure before intelligence: why e-Commerce AI struggles without structured product data

Ecommerce has quietly crossed a threshold. What was once a matter of listing products and driving traffic has become an increasingly complex system of interconnected platforms, data streams, and operational dependencies. Today’s ecommerce business runs on dozens of tools: webshops, PIM systems, ERPs, OMS platforms, analytics suites, ad networks, marketplaces, translation tools, image pipelines, and more. Each system plays a role. Each produces data. And yet, none of them truly understand one another.
This complexity is no longer manageable by humans alone. And while AI is often presented as the answer, most ecommerce teams quickly discover a hard truth: AI without structure, context, and reliable data does not work.

Why modern ecommerce complexity broke traditional workflows

Modern ecommerce is a data-rich environment, but data-rich does not mean insight-rich. The average ecommerce business operates across 20 to 30 systems and manages millions of data points tied to products: attributes, variants, images, descriptions, prices, availability, reviews, returns, performance metrics, and more.

This data is scattered across teams and tools, often inconsistent, outdated, or incomplete. A single product often exists in multiple versions across PIMs, marketplaces, feeds, and ad platforms, but none fully in sync.

As a result, product information differs between webshop, marketplace, and ad feeds. Translations lag behind market expansion. Images are inconsistent or unoptimized for different channels. Feeds break or underperform due to missing attributes. Teams manually copy, clean, and reformat data.

Why AI struggles in ecommerce today

AI systems require structured, contextual data to reason correctly. Without it, they hallucinate, oversimplify, or require extensive manual supervision.

Most critically, the fragmentation of ecommerce data prevents AI from working effectively. Without knowing how variants, markets, and attributes relate, AI outputs become unreliable or require constant human correction.

The real challenge: product data fragmentation

Ecommerce product data is notoriously messy. Attributes are named differently across systems. Variants are modeled inconsistently. Some channels require fields others ignore. Market-specific rules apply to language, sizing, pricing, and compliance.

The scale is significant: millions of data points tied to products across 20 to 30 systems. The diversity spans attributes, variants, images, descriptions, prices, availability, reviews, returns, and performance metrics. Inconsistencies emerge naturally as data flows through different channels and markets.

A missing attribute in one marketplace feed can silently reduce visibility while the same product performs well elsewhere. The hidden operational cost emerges as teams spend time manually fixing data issues rather than focusing on strategy, experimentation, and growth.

What is ‘agentic commerce infrastructure’

Agentic commerce infrastructure is the foundation that enables AI agents to function across ecommerce. It is not an AI agent itself—it is the system that makes agentic systems possible in real-world commerce.

The difference between AI agents and the systems they depend on is critical. Agents are only as capable as the data they operate on. AI agents require a single, structured product truth, reliable context across systems and channels, enriched and up-to-date information, and machine-readable relationships and rules.

Agents can only act independently when they operate on a shared understanding of products, rules, and relationships. Without this infrastructure, agents remain brittle and unreliable. With it, they become operational.

Area	Traditional handling	With Agentic Commerce Infrastructure (With Cernel)
Product data	Data scattered across teams and systems, often inconsistent, outdated, or incomplete	Unified in a single, queryable context layer as single source of truth
Attributes and variants	Named differently across systems, modeled inconsistently	Clearly defined with preserved relationships between data points
Channel consistency	Product information differs between webshop, marketplace, and ad feeds	Changes propagate consistently; attributes, images, texts, and translations remain synchronized
Translations	Lag behind market expansion	Generated automatically from structured product data with market-specific terminology
Images	Inconsistent or unoptimized for different channels	Automatic generation and channel-specific optimization
Feeds	Break or underperform due to missing attributes	Optimized automatically for each channel including LLM-driven search
Data work	Teams manually copy, clean, and reformat data	Dramatically reduced manual work
AI performance	Hallucinates, oversimplifies, or requires constant manual supervision	Agents can operate reliably with structured, contextual data
Team focus	Time spent fixing data issues	Teams can focus on strategy, experimentation, and growth
New products/markets	Bottlenecks during expansion	Faster onboarding of new products and markets

What agentic infrastructure needs to handle (in practice)

Structuring and normalising product data

The first requirement is collecting and normalizing product data. This means restructuring fragmented data into a consistent internal model where products, variants, and attributes are clearly defined, relationships between data points are preserved, market- and channel-specific requirements are mapped explicitly, and data can be queried instantly, even at massive scale.
Attributes like size or material must mean the same thing across regions and channels. This structured foundation ensures that every downstream process—manual or automated—operates on reliable information.

Enrichment and optimisation at scale

Once product data is structured, it must be enriched. Rather than relying solely on what merchants manually provide, enrichment uses AI and external signals to enhance product information automatically.

This includes analyzing historical performance data, reviews, returns, and behavioral signals to identify gaps or weaknesses in product information. Missing attributes, unclear descriptions, or poorly performing variants are flagged and improved. Image recognition extracts visual attributes directly from product images. Text generation creates high-quality product descriptions tailored to different channels and search intents.

Performance data and reviews often reveal gaps in product information that manual processes miss.

Distribution across feeds and discovery systems

Product data today does not live in one place. It must perform across webshops, marketplaces, advertising platforms, comparison engines, and increasingly, LLM-driven search and discovery.

Each channel has its own requirements, schemas, and ranking logic. The infrastructure must optimize product data for each of these outputs automatically. Because all feeds are generated from the same structured context, changes propagate consistently. Attributes, images, texts, and translations remain synchronized, while still being tailored to the demands of each channel.

Data optimised for marketplaces may still fail in LLM-driven search without sufficient context. Structured, high-quality data determines whether products are discoverable at all in LLM-based search systems.

Where Cernel fits into this agentic infrastructure

Cernel is the infrastructure for agentic commerce. At its core, Cernel gathers all product-related data from across the ecommerce stack and structures it into a unified, queryable context layer. This layer becomes the single source of truth that AI agents, applications, and workflows can reliably operate on.

Cernel connects to and works across webshops and headless commerce platforms, PIM, ERP, and OMS systems, marketplaces like Amazon and Google, advertising platforms and product feeds, reviews, returns, and logistics data, and external data sources and search signals. Instead of treating these systems as isolated inputs, Cernel reconciles them into a coherent product model—one that understands how products, variants, attributes, images, languages, and channels relate to one another.

Cernel focuses on gathering, structuring, enriching, and activating product data so AI agents can operate reliably across the ecommerce stack. Rather than being another optimization tool or AI feature, Cernel provides the foundation that makes agentic systems possible.

What this changes for ecommerce teams

For ecommerce organizations, the immediate impact is practical and measurable. Teams experience dramatically reduced manual work on product data, faster onboarding of new products and markets, higher-quality feeds and better channel performance, improved consistency across languages and platforms, and a foundation ready for automation and AI agents.

Teams move from fixing feeds to testing, optimising, and expanding. Instead of spending time fixing data issues, teams can focus on strategy, experimentation, and growth.

Conclusion

AI is transforming ecommerce, but only for those with the right foundations. Before commerce can become agentic, its data must become intelligible.

Data readiness is a strategic decision. In a world where AI agents are becoming part of daily operations, the question is no longer whether to adopt AI, but whether your data is ready for it.

Agentic commerce is less about smarter agents and more about making data intelligible enough for them to work.

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