AI-Ready Product Data Enrichment for Ecommerce
Automate data cleaning, structuring, and enrichment so your products rank in AI-powered search and get more sales on traditional channels.
Traditional search still works. But it's not enough anymore.
AI agents now control product discovery for millions of shoppers—and recommend only products with clean, structured data. Businesses used to struggle with inconsistent supplier data just for their own PDPs. Now that same messy data makes you invisible to AI shopping agents.
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Consistently available data points guaranteed across all products in each category, providing accurate, dependable content.
Publicly accessible data points, but availability may vary depending on the external sources.
Detailed, benefit-driven product information built on genuine insights from real customer experiences and expectations, directly influencing buying decisions.
Consistently available technical specs guaranteed across all products in each category. This is the foundation—accurate, complete, standardized data that AI agents can filter and compare.
Publicly accessible data points sourced from external databases and reviews. Availability varies by product, but we maximize coverage by cross-referencing multiple sources.
Customer-centric translations of technical specs. Built from real customer language patterns, these answer the questions shoppers actually ask.
Generic AI doesn't know which specs matter to your category. We deploy our proprietary AI agent BEACON to scan thousands of customer discussions, expert reviews, and forums—mapping real questions shoppers ask, quantifying buyer segments by market share, and extracting the exact language they use.
LLMs can only rearrange what you already gave them. Our AI agent scrapes manufacturer sites, databases, and expert reviews to fill every gap in your feed. We identify missing critical parameters, normalize inconsistent units, and verify accuracy across sources.
Generic AI creates plausible-sounding fluff. We translate technical jargon into answers to actual customer questions identified by BEACON research.
Examples: Washing machines
- Raw data: "Capacity: 9kg"
- Translation: "9kg = perfect for 3-4 person household; handles weekly laundry in 2-3 loads"
LLMs list features alphabetically or randomly. We order information based on which details affect the most people—benefits that matter to 40% of buyers appear before features relevant to only 8%.
Example: For pressure washers—if "Easy Storage" affects 40% of buyers, it's prioritized over "Professional-Grade Hose" that only 8% care about.
Every claim we make is traceable to a specific source—manufacturer data, expert reviews, or verified databases. If data is missing, we mark it unavailable rather than inventing features.
The difference: Generic AI confidently creates features that don't exist. Our system only outputs what it can prove. That determinism is what makes data agent-ready.
We pull complete technical specs from manufacturer sites, expert reviews, databases, and forums – filling every gap your supplier data leaves behind.
We fix inconsistencies, normalize units, structure attributes, and format everything for UCP/ACP protocol compatibility. This is where flat data becomes machine-readable.
We translate technical specs into customer-centric benefits using proprietary consumer research. AI agents get filterable attributes. Shoppers get answers to real questions. Both get what they need to transact.
No extra workload – just enriched product content ready to go.
Compatible with the tools you already use, from PIMs to e-commerce platforms.
Data feed, embedded product detail widget, or API call integrated with your PIM or warehouse.
What
We transform flat product specs into Agentic-ready data: structured for AI agents to filter and recommend, enriched with customer-centric context so shoppers actually understand what they're buying.
Why
AI agents are rapidly becoming the dominant product discovery channel. If your data isn't machine-readable, you're invisible to external agents (Gemini, ChatGPT, Perplexity, Claude). If it's not customer-friendly, on-site conversions tank. You need both – or you lose on both fronts.
How
Our AI agents map customer behaviour first – what questions they ask, which specs matter, how they segment products. Then we rebuild your catalog: clean the gaps, standardize the chaos, translate jargon into benefits, and format everything for UCP/ACP protocols.
Your customers aren't engineers. They don't know what "17m turn radius" means. They don't speak DIN ratings or sidecut geometry when choosing ski. So they hesitate. They compare endlessly. They abandon cart. Or they buy wrong and return it.
We translate every spec into plain language that answers the questions shoppers actually ask.
Customer-friendly language that builds trust
Contextual guardrails that prevent mismatches
Benefit-driven explanations that drive
ChatGPT, Gemini, Perplexity, and Claude can't recommend what they can't reliably understand. Unstructured marketing copy, incomplete specs, and inconsistent attributes means invisible products.We format your catalog for agentic protocols (UCP, ACP, etc.) with machine-readable attributes, semantic consistency, and quantified matching scores. External AI agents can filter by exact specs, compare apples-to-apples, and rank products mathematically.
Absolute attributes
Complete technical specs
Structured exclusions
To truly enrich product data, you have to know what to enhance. Powered by profound insights into how shoppers discover, evaluate, and select products that meet their needs and expectations, Beacon pinpoints the attributes that actually matter to them. We're the only solution that can define consumer-focused data points, going beyond simple standardization to create enriched content that resonates with buyers and drives purchases.
