73%
Catalogs fail basic AI readability checks
Missing product types, weak descriptions, and inconsistent variants are still the norm.
Shopify catalog intelligence
Get your products surfaced by AI.
Audit and fix your product data so AI-powered shopping tools and LLMs can actually work with it.
- Deterministic scoring with explicit rule deductions
- No writes back to the store without approval
- Theme extension only, so there is no code debt
Built for merchant teams that care about signal over hype
Deterministic scoring, guarded enrichment, and theme-safe delivery without a heavy implementation project.
21 deterministic scoring rules
4 readiness dimensions shared across the app
< 2 min average time to scan a mid-size catalog
0 code theme extension install without theme rewrites
Where shoppers are already asking for products
Search engines ranked your pages.
AI systems read your data.
Ranking still matters. The difference is that AI systems need structured, consistent product data to interpret pricing, variants, availability, and fit correctly.
ChatGPT Perplexity Google AI Overviews Gemini Claude Copilot
Why this has changed
Search still matters. But AI systems parse product data.
Strong SEO still helps discovery. The shift is that answer engines need machine-readable catalog data to classify products, compare them, and cite them accurately.
4.2x
More product detail is needed than classic SEO
Engines need factual attributes they can compare, not surface-level merchandising copy.
Reviewable scoring
No black-box scoring claims
StoreBeam keeps the scoring model deterministic so every deduction is inspectable by the merchant team.
How it works
Audit, prioritize, and ship cleaner catalog data.
The product is opinionated where it should be: deterministic scoring, explicit issue ranking, and zero silent writes back into the store.
01
Scan the entire catalog against readiness rules
Every product is scored across classification, eligibility, variant quality, and structured output. The score is only the summary; the useful part is the audit trail.
- Clear deductions for missing GTINs, weak descriptions, and taxonomy mismatches
- Rule engine shared by the app and background rescans
- Works in real time for merchants and in batch for rescoring
Catalog segment Outerwear
Products 142
Average score 81
Critical issues 14
Theme status Embed live
02
Push the highest-value fixes to the top
Merchants do not need another spreadsheet. StoreBeam turns the audit into a ranked work queue so the first fixes are the ones most likely to improve discoverability.
- One-click workflows for descriptions, alt text, and FAQs
- Guardrailed enrichment based only on existing product data
- No merchant store changes without an explicit approval step
Critical
Images missing descriptive alt text
12 products · bulk generation available
Critical
Variants missing scannable GTIN values
8 products · needs merchant review
High
Descriptions do not expose material and fit attributes
5 products · enrichment suggested
03
Monitor whether ChatGPT, Perplexity, and Google AI are surfacing your products
StoreBeam samples how major AI engines mention your catalog, which products they surface, and what product data they appear to be pulling into answers.
- Weekly monitoring across ChatGPT, Perplexity, Google AI Overviews, and Gemini
- Visibility shown alongside competitor mentions and citation patterns
- Trend reporting that stays honest about signal versus causation
Visibility monitor Last 8 weeks
Merchant mention share
ChatGPT Perplexity Google AI
What changes
What AI sees before and after cleanup.
Before StoreBeam
- Weak product semantics Descriptions sell, but do not explain ingredients, materials, compatibility, or specs.
- Flat taxonomy Products get left in broad buckets that engines cannot meaningfully compare.
- Missing machine-readable context Variants, availability, policies, and FAQs are either incomplete or absent.
After StoreBeam
- Factual descriptions with extraction depth Engines can pull relevant attributes, compare products, and cite them cleanly.
- Aligned Shopify taxonomy Products land in the right category path instead of a generic catch-all.
- Structured output that survives crawling Theme-extension JSON-LD and supporting store settings expose a cleaner machine view.
Product coverage
One app, built around the parts of AI commerce that are actually controllable.
Instead of stacking generic "AI" features, StoreBeam focuses on catalog structure, merchant-controlled enrichment, and honest visibility monitoring.
Readiness engine
Deterministic scoring merchants can defend internally
Every readiness deduction maps to an explicit rule. Teams can see what changed, why it changed, and what is worth fixing first.
Current score 78
Up from 61 after structured data and variant cleanup
Classification coverage
Eligibility completeness
Variant and media quality
Structured output health
Fix queue
High-impact issues rise to the top
The issue queue is ordered by likely discoverability impact, so merchants can focus on the fixes that clean up the machine-readable layer first.
Critical Alt text missing on PDP image sets
Critical Variants missing GTIN values
High Descriptions lack factual attributes
Guardrailed enrichment
Generated copy that stays inside the source data
Descriptions, alt text, and FAQs are drafted from existing product facts. If details are missing, the system flags the gap instead of inventing them.
Before
Soft knit pullover. Great for everyday wear.
After
Merino crewneck sweater with ribbed cuffs, mid-weight knit, and relaxed fit.
Theme extension
Structured data shipped without touching theme files
JSON-LD and FAQ output are delivered through the app embed and block, which keeps implementation cleaner than one-off theme edits or script tags.
{
"@type": "Product",
"name": "Merino Crewneck",
"brand": "Northline",
"offers": { "price": "88.00" },
"gtin": "00851700124561"
}Visibility tracking
Engine monitoring framed with the right amount of caution
Weekly visibility checks show whether engines are surfacing the catalog, but they stay probabilistic and never overclaim attribution.
ChatGPT Perplexity Gemini
Taxonomy alignment
Category cleanup that maps back to Shopify's model
Classification suggestions are tied to concrete taxonomy paths so merchants can standardize product organization instead of creating another custom layer.
Current
Products / Uncategorized / Tops
Suggested
Apparel & Accessories / Clothing / Shirts & Tops / Sweaters
Merchant signals
"We thought our catalog was clean. StoreBeam showed us the machine-readable layer was still inconsistent across most of the store."
DTC skincare brand · 200+ products · post-audit readiness review
47 -> 82 readiness score improvement after prioritized fixes
6 hrs saved compared to a manual catalog and theme audit
1 app for scoring, fixes, schema, and cautious visibility tracking
"The fix queue finally gave merchandising and SEO the same list." Apparel brand · 1.8k SKUs
"We found theme and taxonomy issues we would not have caught manually." Home goods merchant · quarterly cleanup
"The visibility layer is useful because it does not pretend to promise lift." Supplement brand · monitoring customer
Pricing
Pricing that scales with your catalog.
Every plan starts with readiness scoring and issue detection, then expands into enrichment and monitoring as the catalog gets larger and the workflow gets more operational.
Free
See where you stand
$0 /mo
25 products
- AI readiness score
- Issue detection
- Fix guidance
- Product structured data
Starter
Fix what matters
$19 /mo
Unlimited products
- Everything in Free
- Unlimited products
- AI-written descriptions & metadata
Ready to audit
Stop optimizing for ranking systems that no longer explain how people shop.
Run the audit, see where your catalog breaks for AI engines, and decide which fixes are worth shipping. No custom implementation project required.
View on Shopify
Structured for serious catalog teams, without enterprise friction.