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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.
Revenue
AI Recommendation Engine for Adobe Commerce
Recommendations computed from your Adobe Commerce catalog and order history, delivered as a feed and checked on your own data first.
A large Adobe Commerce catalog outgrows hand-picked related products quickly. This engine reads your catalog, orders and customers through a limited integration token and works out what each shopper and each product page should suggest. Your developers get a clean feed to wire into storefront and email, with no new extension to maintain. You approve which catalog fields it may write back.
- Per-customer and per-product suggestions across a large Adobe Commerce catalog
- A developer-ready feed instead of another extension to maintain
- Checked against three months of real orders before launch
$6,000
20–30 working days
Optional maintenance $300/mo
What it is
AI Recommendation Engine for Adobe Commerce connects through an integration token to read your catalog, orders and customers, and computes which products each customer and each product page should suggest. Results arrive as a recurring feed or dashboard for storefront and email, with up to five alerts and agreed catalog fields written back. No extension is installed.
Why businesses buy it
When a catalog runs to thousands of SKUs, merchandisers cannot keep related products current, so shoppers see stale or irrelevant suggestions and leave with smaller baskets. This engine recalculates suggestions from what your customers actually buy, on a schedule, and hands your developers a feed they control. Your Adobe Commerce instance stays clean, with access limited to the resources listed in writing. Because the scope, the test method and the outputs are agreed before you order, your team can plan the storefront work with confidence instead of waiting on an open-ended project.
Problems this solves
Each new Adobe Commerce extension adds upgrade and testing work
No extension is installed. The engine connects through an integration token to read your catalog, orders and customers, so your codebase stays as it is.
Merchandisers maintain related, up-sell and cross-sell links by hand
The engine computes what each customer and each product page should suggest from real order data. Results arrive as a recurring feed, and agreed catalog fields can be written back.
Recommendation output is hard to check against what actually sold
Suggestions are tested on sandbox or test records and checked against three months of history. A dashboard or recurring report then tracks up to 25 defined metrics with up to five alerts.
A good fit if
You run Adobe Commerce with a large catalog, and hand-picked related products cannot keep up with it.
Your team wants recommendations as a feed developers can wire into storefront and email, not another extension to maintain.
You would rather grant an integration token limited to the resources this build needs.
There is at least three months of order history, and an Adobe Commerce admin who can join a 30-minute setup call.
Not the right fit if
Order questions, not product discovery, fill your inbox. See AI Order Support Agent for Adobe Commerce.
Product copy across the catalog needs writing first. See AI Product Description Engine for Adobe Commerce.
You want collection order and featured products chosen from aggregate sales, not per-customer suggestions. See AI Merchandising Agent.
Your store runs on BigCommerce. See AI Recommendation Engine for BigCommerce.
How it works
- An integration token is created in your Adobe Commerce admin, limited to the catalog, order and customer resources we list. A 30-minute call with your admin agrees which catalog fields may be written.
- The engine reads your catalog structure and three months or more of orders and customers, adding browsing behavior from one extra system if you have one.
- It computes two outputs, per-customer recommendation sets and per-product suggestions, guided by up to 25 metrics or questions you define.
- Before anything goes live, the suggestions are compared with three months of what your customers actually bought, and you review the results.
- The feed or dashboard is delivered on your chosen channel for developers to wire into storefront and email, agreed fields are updated in Adobe Commerce, and up to five alerts watch for changes.
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.
- An Adobe Commerce integration token limited to the resources we list.
- Your Adobe Commerce admin for a 30-minute setup call.
- Three months or more of order history.
- Your metric definitions, such as what counts as a repeat purchase.
- Catalog data, and your shipping, returns and refund policies.
- Access to a behavior source if available, and test records or a staging store if you have one.
- Report recipients, the delivery channel, and one decision-maker.
- 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; integration token scope and metrics agreed.
Weeks 1–2
Customer and product recommendation sets modeled on your catalog.
Week 3
Feed, alerts and Adobe Commerce write-back connected.
Weeks 3–4
Suggestions compared with three months of real orders.
Week 4
Your review, then two rounds of changes.
Go-live
Feed live for your developers, handoff recorded, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $6,000 |
| Express | 15–25 working days | $6,900 |
| Rush | 10–20 working days | $7,800 |
What you receive at handover
- AI Recommendation Engine for Adobe Commerce configured and deployed, with no extension installed
- An integration-token connection with documented field mapping
- Access through credentials you control, never sent by email
- A test report on sandbox or test records
- An admin guide and recorded 60-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can reduce the manual upkeep of product link rules
- Helps add personalized suggestions without changing your store's code
- Makes it easier to judge suggestions against your own sales history
- Average order value
- Pull it from Adobe Commerce sales reports for the four weeks before and after the feed goes live.
- Product page conversion rate
- Compare product-page sessions that led to an order in your web analytics for the same two periods.
- Merchandising hours on product links
- Ask the team to log time spent maintaining related and cross-sell links for a month before and a month after.
Before you start: Note last month's average order value and product page conversion rate. Estimate the hours spent each month maintaining product link rules.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build is paid once. Running costs are roughly $20 to $150 a month in your own accounts for AI model usage and the scheduler that refreshes recommendations. Your Adobe Commerce license and hosting are unchanged and not part of this build.
AI usage during the build and testing is billed to your accounts as well.
Ask about running costsCustom requirements
Separate models per website or store view, a second delivery channel, a third data source, or custom Adobe Commerce objects need a written Change Quote. Add a checkout note and we send it to you.
Request a custom versionKeep it running
Monthly maintenance for this edition covers monitoring, fixes and a monthly allowance of change hours, such as retraining after a catalog restructure.
$300/mo (5% of the build price a month, includes 3 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 recommendation extension charges an annual license | An Adobe Commerce partner quotes a custom module | Ours is one fixed price, shown before you buy |
| Integration | Another extension to keep compatible through upgrades | Custom code in your codebase | Ours delivers a feed and writes only agreed fields |
| Access | Broad admin permissions are common | Agreed during the project | Ours uses an integration token limited to listed resources |
| Proof | Measured after launch | Depends on the brief | Ours is checked against three months of your orders first |
Questions about this product
How does AI Recommendation Engine for Adobe Commerce connect to our store?
Through an integration token created in your Adobe Commerce admin, limited to the catalog, order and customer resources we list. Write access covers only the catalog fields agreed in the mapping. Your admin can review or revoke the token at any time, and custom Adobe Commerce objects are excluded.
Do we need to install an extension in Adobe Commerce for recommendations?
Not for this build. The engine runs on its own scheduler in your accounts and delivers a feed or dashboard, plus agreed field updates through the integration token. Your developers decide how storefront and email templates display the results, so no new module is added to your codebase.
What does the Adobe Commerce recommendation build include?
It covers the core recommendation engine plus the Adobe Commerce connection: the integration token setup, catalog mapping, write-back to the fields you approve, a test against three months of your orders, two rounds of changes, a recorded handoff and thirty days of support. The fixed price and the faster delivery options are shown on this page before you buy.
How are Adobe Commerce recommendations tested before launch?
Against three months of your real orders. We compare what the engine would have suggested with what customers went on to buy, and share that comparison with you. Testing uses test records or a staging store where available, and two rounds of changes follow your review.
Who wires the Adobe Commerce recommendation feed into our storefront?
Your developers or agency. This build delivers the recommendation feed or dashboard on your chosen channel and updates agreed catalog fields, ready to display. Building new storefront templates or email designs is outside the Scope Card, though the recorded handoff session walks your team through the feed.
What will the Adobe Commerce engine cost to run?
Roughly $20 to $150 a month in your own accounts for AI model usage and the scheduler that refreshes recommendations. Your Adobe Commerce license and hosting are separate. Optional monthly maintenance starts after the 30 days of support end. It covers monitoring, fixes and change hours, such as retraining after a catalog restructure.
Guides on this topic
All guides Give a large catalog recommendations that keep up with it.
Order AI Recommendation Engine for Adobe Commerce and your catalog and order history become a tested recommendation feed your developers can wire in, delivered on a fixed date through a token limited to what it needs.
Works well with
Catalog
AI Merchandising Agent
Recommends collection order and featured products from sales and stock data.
Operations
AI Ecommerce Analytics
Answers questions about sales, margins, cohorts and channels in plain language.
Catalog
AI Catalog Optimization
Finds and fixes missing attributes, duplicates and inconsistencies in your catalog.