Websites, Stores and Search
Generative engine optimization: how to get cited by AI search
What generative engine optimization really is, what the research shows, and a step-by-step plan to get your pages cited by AI search.
Catalog
AI Merchandising Agent
Collection order and featured products recommended from your own sales and stock data, delivered as a recurring report.
Deciding which products to feature and how to order your collections is usually a gut call made on a busy Monday. This agent reads your sales and stock history and recommends collection order and featured products each week, so the spotlight goes to items that sell and are actually in stock. It reports to you and never changes your storefront on its own.
- Collection order recommended from real sales and stock data
- Featured products chosen from items that sell and are in stock
- Recommendations delivered by email, Slack or dashboard
$2,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Merchandising Agent recommends how to order your collections and which products to feature, reading up to three read-only data sources of sales and stock history to decide what deserves the spotlight this week. It delivers a dashboard or recurring report rather than changing your storefront directly. Your team stays in charge of the storefront.
Why businesses buy it
Merchandising decisions shape what shoppers see first, and that drives a large share of what they buy. Featuring a product that is nearly out of stock, or burying a best-seller, quietly costs revenue every day. This agent bases those calls on your own data, consistently, every week. Revenue and conversion reporting shows the effect. The written scope sets out the data sources, the recommendations and the delivery channel before you order.
Problems this solves
Collection order is set once and then left for months
AI Merchandising Agent recommends how to order your collections each week. It reads up to three read-only sources of sales and stock history to decide what deserves the spotlight.
Featured products are chosen by habit, not by data
Recommendations are based on recent sales and stock, not guesswork. The dashboard or recurring report explains which products to feature this week and the reason for each choice.
Products with deep stock stay buried while bestsellers run low
Stock history is part of the analysis, so items with healthy stock can be suggested for promotion. Your team stays in charge and changes the storefront itself.
A good fit if
You decide what to feature on your homepage or category pages by gut feel, and want that decision grounded in real sales and stock data instead.
Your collections rarely get reordered because no one has time to check which products are actually moving.
You can give read-only access to your sales and stock data sources, plus at least three months of history to compare against.
You want a recurring recommendation delivered to email, Slack or a dashboard, with alerts when something changes sharply.
Not the right fit if
Your real problem is catalog data quality, not merchandising decisions, such as duplicate listings or missing attributes. See AI Catalog Optimization built for finding and fixing those issues.
You want recommendations aimed at individual shoppers, not collection-level curation decisions. See AI Recommendation Engine, which personalizes suggestions from behavior and purchase history.
You want the recommended changes applied to your storefront automatically, not delivered as a report. See Shopify AI Product Agent, which reads and writes collections through the Shopify Admin API.
You need more than three read-only data sources or more than five alerts, for example separate reports across five storefronts. Add a note at checkout for a Change Quote, or start from our Custom AI enquiry form.
How it works
- You give us read-only access to up to three data sources covering sales, stock and traffic, plus your own definitions of what 'performing well' means.
- The agent compares current sales velocity and stock levels against at least three months of your historical data to separate a real trend from noise.
- It builds a recommended collection order and a featured-product list for each collection you cover, with the reasoning stated for every choice.
- Results are compiled into one dashboard or recurring report, delivered to the channel you chose: email, Slack or a web dashboard.
- Up to five alerts are set on the moments that matter most, such as a featured product about to run out of stock.
- Your team reviews the recommendation and applies it, either by hand or through a connected write-access agent if you have one.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Read-only access to each sales, stock or traffic data source you want the recommendations built from.
- Your own definitions of the key metrics, such as what counts as a top performer or a slow mover.
- At least three months of historical sales and stock data, so trends can be checked against real history.
- The report recipients and the delivery channel you want, whether that is email, Slack or a dashboard.
- The name of the person who reviews the first recommendation and confirms the alert thresholds.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker on your side who can sign off during the build.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake verified, read-only access to your sales and stock data confirmed.
Weeks 1–2
Metric definitions and the recommendation logic built.
Week 3
Dashboard, report and alert delivery channel connected.
Weeks 3–4
Results checked against three months of your historical data.
Week 4
Your review and two rounds of changes.
Go-live
Live, first full recommendation delivered, recorded handoff session, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $2,900 |
| Express | 15–25 working days | $3,400 |
| Rush | 10–20 working days | $3,900 |
What you receive at handover
- AI Merchandising Agent configured and deployed, producing weekly recommendations
- Workflow, prompt and guardrail configuration for merchandising rules
- Read-only connections to up to three data sources
- One dashboard or recurring report with up to five alerts (recommendations; storefront changes are made by your team)
- A test report checked against three months of your historical data
- An admin guide, the recorded 60-minute handoff session and 30 days of post-launch support
Expected benefits and how to measure them
- Helps keep your storefront fresh with a clear weekly plan.
- Can make better use of the stock you already hold.
- Sales from featured products
- Compare revenue from featured items in your store reports for the month before and after you follow the recommendations.
- Collection page conversion
- Use store analytics to compare conversion on reordered collections over the same periods.
- Slow-moving stock
- Count products with no sales in the past month, from your inventory report, before and after.
Before you start: Record conversion on your main collections and a list of slow-moving products. Note how often collections are reordered today.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is one-time. Running the report is billed to your own accounts, mostly AI model usage and the scheduler that runs it: typically $20 to $150 a month.
AI usage during the build and test phase is billed the same way, so your AI provider and hosting stay in your own name throughout.
Ask about running costsCustom requirements
Need a fourth data source or recommendations across more than one storefront beyond the Scope Card? Add a note at checkout and we return a written Change Quote before that extra scope starts. Larger or bespoke projects go through the Custom AI request form instead.
Request a custom versionKeep it running
Optional monthly maintenance covers monitoring, fixes, model and platform updates, a monthly health report and monthly change hours for changes such as a new metric or a seasonal collection rule. It starts once the 30 days of included support end and can be cancelled in any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A merchandising app charges a monthly fee tied to store size | A merchandising consultant quotes a retainer per month | Ours is one fixed price, shown before you buy |
| Scope | A merchandising app applies rules you configure yourself | An agency agrees scope during the project | Ours is built around the metrics you define |
| Data behind it | Most apps rank by clicks, not verified sales and stock history | An agency build depends on what was agreed | Ours checks three months of your own sales and stock data |
| Delivery | An app installs in a day but the rules stay generic | A consultant retainer usually runs monthly, ongoing | Ours has a fixed delivery time shown before you buy, with faster options |
| Alerts | Most apps show a dashboard you have to check yourself | An agency build depends on what was agreed | Ours includes up to five alerts for the moments that matter |
Questions about this product
What does the AI Merchandising Agent cost?
Each delivery speed has one fixed price. The price panel shows standard, express and rush side by side before you pay, and the cart confirms your delivery date. Running costs of roughly $20 to $150 a month are separate and paid to your own providers.
What does the AI Merchandising Agent cost to run each month?
Typically $20 to $150 a month, covering AI model usage and the scheduler that runs the recurring recommendation, billed to your own accounts. There is no separate platform fee from us. Optional monthly maintenance for ongoing metric changes is priced on this page.
Does it change my collections automatically?
No. It reads from read-only data sources and delivers a recommendation, either as a dashboard or a recurring report, that your team applies. If you want the recommended order applied automatically, pair it with a write-access build such as Shopify AI Product Agent.
How does it decide what counts as a top performer?
From the metric definitions you set during intake, checked against at least three months of your own sales and stock history. That keeps the recommendation grounded in your store's real pattern rather than a generic 'best-seller' assumption borrowed from another business.
Can it cover more than one collection or storefront?
Yes, within the three read-only data sources and 25 defined metrics included in this build, which can span more than one collection at once. A recommendation spanning more storefronts than that, or a full regional rollout, is scoped through a written Change Quote before the extra work starts.
What happens once the AI Merchandising Agent's 30 days of support end?
You can move to monthly maintenance, which includes monitoring, fixes and a monthly allowance of change hours. It is optional, bills monthly and can be cancelled in any month. Without it, the report keeps running; you simply lose included changes.
Do I own the recommendation logic once it is live?
Yes. The metric definitions, scan logic and delivery schedule are built inside your own connected accounts, so nothing about ongoing reporting depends on us staying involved after the 30 days of support. Cancelling maintenance later does not remove anything already live.
Guides on this topic
All guides Let your sales data decide what gets featured.
Order the AI Merchandising Agent and get a recurring collection and featured-product recommendation built from your own sales and stock, so the spotlight goes where it earns.
Works well with
Catalog
AI Product Description Engine for Adobe Commerce
Writes on-brand product descriptions at catalog scale, connected natively to Adobe Commerce so it reads and updates catalog products, attributes and categories.
Catalog
AI Product Description Engine for WooCommerce
Writes on-brand product descriptions at catalog scale, connected natively to WooCommerce so it reads and updates products, variations, categories and attributes.
Catalog
AI Product Description Engine for BigCommerce
Writes on-brand product descriptions at catalog scale, connected natively to BigCommerce so it reads and updates catalog products, variants and categories.