Source
PIM
Product Information
sku_count124,891
complete47%
sync_age11d
Missing—53% of rows missing required attributes
Paladio Catalog Agents · Built on Lattice
Make every product
Make every product
AI-ready.
Autonomous agents turn raw, inconsistent product data into clean, classified, compliant, agent-readable records — so your products show up wherever buyers are.
The problem
Four systems. Four schemas.
Four systems. Four schemas.
None of them agent-readable.
Every source tells a partial story — and none of them expose an endpoint an AI agent can reason over.
Source
feed.csv
Marketplace export
rows88,210
schemav2 / v4 mixed
rejections4,117
Inconsistent—Two schemas in one file
Source
Shopify
Storefront
products62,004
hazmat_tag—
faqs0
Compliance—Hazmat, tax, multipacks unvalidated
Source
Search
Discovery layer
queries_served2.1M / mo
no_result_rate18.4%
agent_calls0
Invisible—No endpoint an AI agent can read
Connect once
API-first. MCP-native.
API-first. MCP-native.
Instantly deployable.
Connect your sources once. Every channel — and every agent — speaks the same canonical schema.
mcp.paladio.ai / v1200 OK
$npm i @paladio/catalog-agents
✓ connected · MCP endpoint ready
$ca.connect({ source: "shopify" })
→ 62,004 products indexed in 0.43s
How it works
From raw SKU to agent-ready.
Catalog Agents enrich, classify, and validate every product, then publish a semantic graph an agent can traverse — not just another flat feed.
01 · DISCOVERY
Structured attributes, richer titles
- Category taxonomy + brand normalization
- Spec extraction from images & PDFs
- Agent-readable JSON-LD + schema.org
+41%discovery score uplift
02 · COMPLIANCE
Hazmat, tax, multipack validation
- UN numbers + hazard class per SKU
- Tax code (HTS / HSN / GST) per region
- Multipack & variant integrity checks
99/100compliance score
03 · CONTENT
Use-cases, FAQs, agent answers
- “Works for…” statements per product
- Generated Q&A for voice + chat
- Comparison vectors to similar SKUs
12×more agent-answerable queries
The reasoning layer
Don't just feed agents data — let them reason over your catalog.
A flat feed answers what. Catalog Agents answer which one, for whom, and why — turning your SKUs into a structured graph an agent can traverse, compare, and recommend over.
Raw row · sku_4419
title: "power drill 18v"
cat: "misc"
hazmat: null
desc: —
agents
Enriched · sku_4419
title: "DeWalt 18V Brushless Drill/Driver — 2-speed"
cat: Power Tools › Drills › Cordless
hazmat: UN3480 · Class 9
use_case: "drilling masonry, hardwood, metal"
01
Semantic graph, not a flat feed
Every SKU becomes a node connected to its category, materials, use-cases, compatible accessories, and comparable products — so agents reason over relationships, not just rows.
02
Multi-hop queries, single endpoint
"Show me a cordless drill under $200, hazmat-shippable to Texas, that pairs with the bits I bought last month." One MCP call, reasoned across the graph.
03
Grounded, with citations
Every agent answer cites the SKU, attribute, and source it reasoned from. No hallucinated specs. No phantom products. Auditable by design.
04
New surfaces, no rewrites
Voice, chat, autonomous shopping agents, in-context AI search — same graph, same protocol, no per-channel rewrites.
User
"I need a drill for masonry that ships to my warehouse in TX by Friday."
Agent · catalog.reason()
→ filter(use_case ⊇ "masonry") · 412 SKUs
→ join(hazmat.shippable_to: "TX") · 287 SKUs
→ join(stock.warehouse_eta ≤ "Fri") · 14 SKUs
→ rank(review_score · price)
→ join(hazmat.shippable_to: "TX") · 287 SKUs
→ join(stock.warehouse_eta ≤ "Fri") · 14 SKUs
→ rank(review_score · price)
DeWalt DCD996B (sku_4419) — 18V, hammer mode, ships Wed from Dallas DC. Cite: spec.uses ⊃ masonry · hazmat.UN3480 · stock.dfw01.eta=2026-05-06.
Measurable impact
See exactly how your products rank — and how they improve.
A live Catalog IQ score across discovery, compliance, and content — re-scored the moment agents go to work, and tracked across every channel.
Catalog IQ
Before
sku_4419 — sampled across 62,004 rows
Discovery34 / 100
Compliance41 / 100
Content22 / 100
Overall32— baseline
Catalog IQ
After
Catalog Agents active · re-scored 0.12s
Discovery92 / 100
Compliance99 / 100
Content88 / 100
Overall93▲ +61
Channel
Marketplaces
Amazon, Walmart, eBay
+54%rank index
Channel
Storefront
Shopify, BigCommerce
+38%conversion
Channel
Search
Google, Bing
+72%impression share
New surface
Agentic Commerce
Agent-driven surfaces
∞from 0 coverage
Case study · Voomi Supply
Voomi Supply publishes supplier catalogs across DTC and Amazon — Catalog Agents took enrichment from a manual bottleneck to 75% faster publishing.
75%
Faster catalog publishing
70%
Reduction in operating cost
80%+
Product match accuracy (Profitero)
1,000+
Datasets scaled across (Profitero)
Deploy on your infrastructure
Procure through AWS. Run in your own account.
aws
Available now · AWS Marketplace
Procure with one click. Run in your VPC.
Catalog Agents ships through AWS Marketplace as a private SaaS offering — drawn from your existing AWS commitment, deployed in your account, with data that never leaves your boundary.
Built on Lattice
Reasoning over your catalog, not just storage.
Catalog Agents connect your feeds, build a semantic product graph, and validate compliance — automatically.
Flux
Connect
MCP · APIs · databases · SaaS · events
Fabric
Understand
Context graph · task-scoped assembly · reusable memory
Fidelity
Trust
Evals · governance · lineage · audit
Forge
Build
Runtime · orchestration · abstraction
Not central here
More from Paladio
Every product runs on the same foundation.
Get started
Make your catalog AI-ready.
A 30-minute setup. A measurable AI-readiness score in under an hour — across every channel.