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Be the brand AI agents recommend first

Google and OpenAI agents already shop for 700 million consumers. We help Australian retailers win that recommendation, starting with your catalogue.

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Agentic shopping isn’t coming — it’s here

Jan 2026

UCP

protocol launched

GoogleWalmartTargetShopify

Google launches Universal Commerce Protocol

Source · Google UCP

Retailers who aren’t agentic-ready risk falling behind.

The ones who are? They’re capturing market share right now.

the shift - Your customers aren’t shopping anymore — their AI agents are

When a customer asks ChatGPT to “find me the best running shoes under $200”, the AI agent scans product catalogues, compares attributes, and recommends — all without visiting your website. If your catalogue data is incomplete, inconsistent, or stale, your products won’t be recommended. You become invisible.

$5T

in commerce AI agents could mediate by 2030

Disintermediation

AI agents become the storefront. Retailers lose direct customer relationships, loyalty programme activation, and the data that powers personalisation.

Source · McKinsey

393%

YoY growth in AI-driven retail traffic in Q1 2026

The data quality gap

Most retail catalogues have missing descriptions, outdated inventory, inconsistent taxonomy, and no trend-aligned content. AI agents can’t recommend what they can’t understand.

Source · Adobe Digital Insights

81%

of retail executives say AI will weaken brand loyalty

The race is on

Your competitors are already preparing their catalogues for Google’s Universal Commerce Protocol. Every day without action is market share lost to retailers with richer product data.

Source · Deloitte 2026 outlook

We knew agentic shopping was coming but had no idea where to start. Embeddings audited our entire product catalogue in days and showed us exactly where we were falling short — missing descriptions, stale inventory data, zero trend alignment. Their enrichment pipeline transformed our catalogue from a static spreadsheet into a living, AI-ready asset.

Australian retail executive

Head of Digital, National Retailer

services - Your catalogue is your competitive moat — we make it unassailable

From audit to real-time optimisation, our four services transform your product catalogue into an asset AI agents can read, trust, and recommend. No other consultancy in Australia has this combination of LLM pipeline expertise and data engineering capability.

BeforeIncomplete

Blue Dress

GTIN

A blue dress for women.

coverage
thin
agent signal
unclear
colour?silhouette?occasion?material?

Last updated: 8 months ago

1Audit
2Freshness
3Enrichment
4Optimisation
AfterAgentic-ready

Women’s A-Line Midi Dress — Sapphire Blue, Taylor Swift-inspired

GTIN0614141123456

Flattering A-line midi dress in sapphire blue crepe. Features a fitted bodice with subtle darting, a flowing midi-length skirt, and concealed side zip. Inspired by the blue dress trend popularised by Taylor Swift. Perfect for weddings, racing carnivals, and cocktail events. Machine washable. Available in sizes 6–18.

coverage
complete
agent signal
clear
Sapphire BlueA-LineMidiWedding GuestCrepeSizes 6–18
Trending: Taylor Swift blue dress

Last updated: 2 hours ago

implementation loop

The before-and-after shift is delivered in four connected stages

The overview above shows the catalogue outcome. This loop shows how we move the data from audit evidence to live optimisation without turning the work into a generic AI programme.

  1. 01risk register

    catalogue audit

    Find blockers

    Find missing identifiers, thin content, stale data, and feed risks before agents rank the catalogue.

  2. 02schema map

    catalogue freshness

    Repair records

    Connect ERP, POS, and inventory changes so product truth reaches commerce surfaces quickly.

  3. 03review queue

    catalogue enrichment

    Add buying context

    Turn sparse records into complete attributes, clearer descriptions, and agent-readable taxonomy.

  4. 04freshness loop

    contextual optimisation

    Keep it current

    Fold trend signals back into product content while demand is still active.

1

catalogue audit

See every gap before your competitors exploit it

We analyse your entire product catalogue against Google Merchant Centre specifications and agentic commerce standards. The audit identifies missing descriptions, malformed GTINs, inconsistent taxonomy, and thin data — then produces a prioritised remediation plan ranked by revenue impact.

catalogue audit

1

Find missing identifiers, thin content, stale data, and feed risks before agents rank the catalogue.

2

catalogue freshness

Fresh data keeps you in the recommendation set

AI agents penalise outdated catalogues. We build real-time integrations from your ERP, POS, and inventory systems so stock levels, pricing, and product status are always current. A fresh catalogue means your products stay in the recommendation set.

catalogue freshness

2

Connect ERP, POS, and inventory changes so product truth reaches commerce surfaces quickly.

3

catalogue enrichment

From thin listings to rich, AI-readable content

Our LLM pipelines transform sparse product data into rich, brand-aligned descriptions, categories, and attributes. Thousands of SKUs enriched in hours, not months. If an AI agent can’t understand your product data, your products don’t exist in agentic commerce.

catalogue enrichment

3

Turn sparse records into complete attributes, clearer descriptions, and agent-readable taxonomy.

4

contextual optimisation

A living catalogue that captures demand as it shifts

We connect your catalogue to live trend signals — Google Trends, social platforms, news cycles — so product descriptions evolve with what consumers are searching for right now. When cultural moments create demand spikes, your products are positioned to capture that intent before competitors.

contextual optimisation

4

Fold trend signals back into product content while demand is still active.

Be the brand AI agents recommend first

The retailers preparing their catalogues today are building advantages that compound tomorrow.

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