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 Catalog Optimization
A recurring report of missing attributes, duplicate listings and catalog inconsistencies, with the exact fix for each.
Missing sizes, duplicate listings and products filed in the wrong category quietly cost you sales and ad approvals. This build reviews your catalog on a recurring schedule, finds those gaps and inconsistencies, and gives you a prioritized list with the exact fix for each one. Alerts tell you when new problems appear, so the catalog stays clean as it grows.
- Missing attributes, duplicates and miscategorized products found
- A prioritized fix list with the exact change needed
- Alerts when new gaps or duplicates appear
$2,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Catalog Optimization reviews your product catalog on a recurring schedule, reading up to three read-only data sources to find missing attributes, duplicate listings and inconsistent categorization, then delivers a dashboard or report with the specific fix for each issue. You decide which fixes to apply.
Why businesses buy it
Catalog problems are hard to see and easy to ignore until they hurt: shoppers cannot filter, feeds get rejected by marketplaces and ad platforms, and duplicate pages split search traffic. Auditing a large catalog by hand is slow, so it rarely happens. This build checks everything on a schedule and tells you exactly what to fix first. The written scope sets out the data sources, the checks and the report format before you order.
Problems this solves
Missing attributes break filters and hide products from shoppers
AI Catalog Optimization reviews your catalog on a schedule and finds missing attributes. The report names each gap and the specific fix, so filters can work properly.
Duplicate listings split reviews and confuse shoppers
It spots duplicate listings across up to three read-only data sources. Each one comes with a suggested fix, and you decide which fixes to apply.
Categories drift as different people add products
Inconsistent categorization is flagged in a dashboard or recurring report. Up to five alerts can tell you when new issues appear between scheduled reviews, so problems do not build up.
A good fit if
Your catalog has grown through imports, migrations or several people editing it, and no one is sure what is actually inconsistent.
You suspect duplicate listings or missing attributes are hurting search and filtering, but auditing the whole catalog by hand is not realistic.
You can name your key metrics and give read-only access to your catalog data source, plus at least three months of history.
You want a recurring report and alerts sent to a channel like email, Slack or a dashboard, not a live tool you operate yourself.
Not the right fit if
Your catalog structure is already clean and the real gap is that your descriptions read badly. See AI Product Description Engine, which writes on-brand copy at catalog scale.
You already know what needs fixing and want it applied directly and ongoing, not reported. See Shopify AI Product Agent, which reads and writes tags and collections through the Shopify Admin API.
You want a curation decision, not a cleanup, deciding what to feature and where. See AI Merchandising Agent, which recommends collection order and featured products from sales data.
You need more than three read-only data sources or more than five alerts, for example separate reports across five regional catalogs. 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, your definitions of the key metrics, and at least three months of historical catalog data.
- The engine scans every listing against those definitions, checking for missing attributes, near-duplicate titles and categorization that does not match your own rules.
- Each issue found is checked against three months of your historical data, so a flagged item is a real inconsistency, not normal seasonal variation.
- 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 patterns that matter most to you, such as a spike in newly duplicated listings.
- Every flagged issue comes with the specific fix, the corrected attribute value or the listing to merge, so your team applies it in minutes, not hours.
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 catalog data source you want reviewed.
- Your own definitions of the key metrics, such as what counts as a duplicate or an incomplete listing.
- At least three months of historical catalog data, so patterns 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 report 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 data sources confirmed.
Weeks 1–2
Metric definitions and the scan logic built against your catalog.
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 report 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 Catalog Optimization configured and deployed on a recurring schedule
- Workflow, prompt and guardrail configuration for catalog checks
- Read-only connections to up to three data sources
- One dashboard or recurring report with a specific fix per issue, plus up to five alerts (recommendations; you apply fixes)
- 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
- Makes it easier to keep a growing catalog tidy and searchable.
- Helps your team fix the right issues first instead of hunting for them.
- Open catalog issues
- Track the issue count in the report each cycle, starting from the first full review.
- Products with complete attributes
- Compare the share in a catalog export before launch with one two months later.
- Filter and search use leading to a product view
- Compare your store analytics for the month before launch and two months after.
Before you start: Export your catalog and note known duplicates and missing attributes. Record how long catalog checks take your team 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 more than five alerts 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
Monthly maintenance covers monitoring, fixes, model and platform updates, a monthly health report and monthly hours for changes such as a new metric definition or an extra alert. Your change hours scale with the build. 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 data-quality app charges a monthly fee tied to catalog size | A data agency quotes a project fee for a one-time audit | Ours is one fixed price, shown before you buy |
| Scope | A data-quality app flags issues against generic rules | An agency agrees scope during the project | Ours is built around the metrics you define |
| Recurrence | A one-time audit tells you today's problems, not tomorrow's | An agency audit is usually a single engagement | Ours runs on a recurring schedule with alerts built in |
| Delivery | A data-quality app installs in a day but the rules remain generic | An agency audit usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| The fix | Most tools flag the problem, not the correction | An agency build depends on what was agreed | Ours includes the specific fix for every issue found |
Questions about this product
What does AI Catalog Optimization 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 AI Catalog Optimization cost to run each month?
Typically $20 to $150 a month, covering AI model usage and the scheduler that runs the recurring report, billed to your own accounts. There is no separate platform fee from us. Optional monthly maintenance is there if you want ongoing metric changes made for you.
Does it fix the catalog issues itself?
It finds each issue and reports the specific fix, but this build reads from read-only data sources rather than writing back to your store directly. Your team applies the correction, or a write-access build such as Shopify AI Product Agent can apply it automatically instead.
How far back does the historical check go?
At least three months of your own catalog data, which you provide during intake. Results are checked against that history before launch, so a flagged item reflects a genuine inconsistency in your catalog rather than a normal seasonal pattern that happens every year around the same time.
Can it review more than one catalog source at once?
Yes, up to three read-only data sources are included in this build, for example your storefront platform, a feed file and a spreadsheet export. A fourth source is available through a written Change Quote, priced before that extra work starts.
What happens once AI Catalog Optimization's 30 days of support end?
You can move to monthly maintenance, which includes monitoring, fixes, a monthly health report and monthly hours for changes. 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 reports and the underlying rules?
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 Find out what is actually wrong with your catalog.
Order AI Catalog Optimization and get a recurring, prioritized report of missing attributes, duplicates and the exact fix for each, so your catalog stays clean as it grows.
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