Paladio Catalog Agents · Built on Lattice

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 commerce is happening, including the surfaces that don’t exist yet.

Book a demo → See how it works

Flux · ConnectFabric · UnderstandFidelity · TrustForge · Build

AI-readiness score rising from 32 to 93 across discovery, compliance, and content, with channel uplift — AI-readiness score · baseline 32 → 93 after enrichment

The problem

Most catalogs are incomplete, inconsistent, and invisible to modern AI.

Product data lives across PIMs, spreadsheets, marketplace feeds, and storefronts — each in its own schema, none of them readable by an agent. A flat product feed answers what; agents need to reason over which one, for whom, and why.

Where the data lives

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

PIM

Product Information

sku_count124,891

complete47%

sync_age11d

Missing—53% of rows missing required attributes

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

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.
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"

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.

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.

Grounded, with citations

Every agent answer cites the SKU, attribute, and source it reasoned from. No hallucinated specs. No phantom products. Auditable by design.

Built for the surfaces that don't exist yet

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)

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

Google, Bing

+72%impression share

New surface

AI 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.

Voomi Supply · HVAC marketplace · DTC + Amazon

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.

View on Marketplace →

ProcurementCounts toward EDP

ComplianceSOC 2 · ISO 27001

DeploymentPrivate SaaS in your VPC

BillingHourly or annual

Built on Lattice