Auckland-headquartered. ANZ-focused.
hello@atlasstudios.agency
We prepare ANZ businesses to be found, understood and recommended by the AI agents their customers now shop with.
Agentic commerce is the shift from customers browsing storefronts to AI agents querying structured data on their behalf.
ChatGPT, Google AI Mode and Perplexity now surface products directly in conversation, and they read your catalogue rather than your website. If your data isn't machine-readable, you're invisible at the moment the decision gets made.
Learn more about making your brand legible to the machines your customers are asking.
You're probably already selling to agents and don't know it yet.
Revenue is arriving through AI surfaces and being filed under direct traffic, because nothing in standard reporting separates a customer from a crawler from an agent.
The first question isn't whether to invest in AI work, it's whether you can see what's already happening and measure attribution.
Search became conversation.
Customers ask an assistant what to buy instead of comparing tabs. The agent reads structured product data, checks price and availability, and returns a shortlist. There's no page one to rank on. Either your products are in the answer or they aren't, and an assistant can't infer from a photograph the way a browsing customer can.
That's what we build.
Measurement, so you can see the channel.
Structured attributes, so agents can read your catalogue.
Policies in text an agent can verify, because when it can't confirm your shipping or returns, it defaults to a competitor it can.
Unglamorous foundations that improve filtering, feeds and conventional search on the way past.
From seeing what AI is already sending you, to the data and loops that decide whether assistants recommend you at all.
Agentic Measurement
AI and agent traffic separated into its own channel, and conversion recalculated against genuine human sessions.
Product Attribute Schema
A category-specific attribute taxonomy and live metafield architecture. The asset that survives whatever tool you choose later.
Attribute Enrichment
Machine-generated attributes with confidence scoring and human review, piloted on your top products, then scaled across the catalogue.
Agent-Readable Content
Shipping, returns and sizing restructured as text an agent can parse, with schema markup and a published llms.txt.
Catalogue Integrity & Signal Loops
Automated hygiene, returns and review data fed back into attributes, and enrichment that happens at product creation.
Team enablement
Team empowerment and enablement to allow your internal teams to ship simple features, safely.
Atlas Studios works agentic commerce in three stages: see it, be found, compound it.
First we make AI traffic and AI visibility measurable, so the conversation runs on numbers from your own dashboard rather than someone's forecast.
Then we make the catalogue legible to agents and assistants.
Then we build the loops that keep it that way without manual effort.
Your reporting currently can't tell a customer from a crawler from an agent. We build a measurement layer alongside Shopify Analytics that you control. Qualified sessions based on real interaction signals, conversion rate recalculated honestly, and AI referrers reported as their own channel rather than buried in direct.
We build 30 to 40 questions your customers genuinely ask, from support tickets and site search rather than invention, then run them across five assistants.
Do you appear? Are you described accurately?
Is your pricing right? And who is being recommended in your place?
Most brands have never checked.
A category-specific attribute taxonomy built around the questions customers actually ask: use case, fit, occasion, environment, care.
Then live metafield architecture in Shopify with validation. Buying an enrichment tool before this produces a catalogue of confidently wrong attributes, which is worse than none.
Attributes generated from imagery and existing copy with confidence scoring, every one human-reviewed before publication, piloted on your top products then scaled.
Alongside it, shipping, returns and sizing moved out of images and PDFs into text an agent can actually verify.
Automated catalogue hygiene so an assistant never recommends something out of stock.
Returns and review data mined for the attributes that were wrong and fed back into the product page. Enrichment that runs at product creation, so new ranges are legible from day one instead of drifting.
Your reporting currently can't tell a customer from a crawler from an agent. We build a measurement layer alongside Shopify Analytics that you control. Qualified sessions based on real interaction signals, conversion rate recalculated honestly, and AI referrers reported as their own channel rather than buried in direct.
We build 30 to 40 questions your customers genuinely ask, from support tickets and site search rather than invention, then run them across five assistants.
Do you appear? Are you described accurately?
Is your pricing right? And who is being recommended in your place?
Most brands have never checked.
A category-specific attribute taxonomy built around the questions customers actually ask: use case, fit, occasion, environment, care.
Then live metafield architecture in Shopify with validation. Buying an enrichment tool before this produces a catalogue of confidently wrong attributes, which is worse than none.
Attributes generated from imagery and existing copy with confidence scoring, every one human-reviewed before publication, piloted on your top products then scaled.
Alongside it, shipping, returns and sizing moved out of images and PDFs into text an agent can actually verify.
Automated catalogue hygiene so an assistant never recommends something out of stock.
Returns and review data mined for the attributes that were wrong and fed back into the product page. Enrichment that runs at product creation, so new ranges are legible from day one instead of drifting.
The number of places your product needs to be legible is going up. There is no single new channel to master. There's a shift from optimising one funnel to being coherent everywhere.
Buying is moving into conversation, and AI is here to stay.
80% of boards in New Zealand report that they can't track ROI on AI work.
We are changing that.
See What's Already Happening
AI revenue separated from direct traffic, so you watch the channel grow instead of inferring that it exists.
A Conversion Rate You Can Defend
Bot sessions out of the denominator, so the number you take to your board doesn't need a caveat.
Clean Data Foundations
A category attribute schema you own, an asset that outlives whatever enrichment tool you choose later.
Found and Described Correctly
Not just whether assistants recommend you, but whether they get your price, stock and positioning right.
Wins Before AI Pays Off
Better on-site filtering and feed quality land first, and usually pay for the work on their own.
Early Mover Advantage
The surfaces aren't competitive yet. Being early is cheap now and expensive to retrofit later.
The platforms, protocols and behaviours are live now.
The only question is whether your data is ready for them.
Attributable & measurable
Being able to measure AI spend and effectiveness is table stakes.
Agent-led buying
AI agents now buy on a shopper's behalf. Your store has to be legible to them.
Machine-readable data
Clean product, pricing and inventory data decide whether AI surfaces you at all.
Agentic commerce is when AI agents act on a shopper's behalf.
For example, researching products, comparing options and in some cases completing the purchase, without the customer browsing a website directly. For merchants it shifts the job from ranking on a results page to being readable and recommendable by the assistant itself.
You don't train the model. You make your brand legible to it. That means structured product data an agent can parse without guessing, content written as self-contained answers that survive being quoted alone, precise and consistent entity naming, and presence on the sources assistants actually read.
Talk to our team to find out more.
Through data, not advertising.
AI tools read structured product feeds and schema markup, then check price and availability before recommending. Products with incomplete identifiers, missing attributes or stale inventory get skipped. The work is unglamorous catalogue hygiene, done properly.
The discovery side is already live here. Customers use ChatGPT, Gemini and Perplexity to research before buying, and those tools read structured data regardless of where you trade. Doing the data work now is cheap; retrofitting it once agentic channels open is not.
Atlas Studios is a Shopify Plus Partner in Auckland, working on agentic commerce for businesses across New Zealand and Australia.
We make AI revenue measurable, structure product data so agents can read it, and build the loops that keep a catalogue legible as it grows.
Talk to our team to understand what's possible.
We can work with you to identify efficiency gaps and roll out systems that close them.
