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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 BigCommerce
BigCommerce catalog, orders and customers turned into tested product recommendations, with permissions scoped to what the engine needs.
Your BigCommerce store already knows what each kind of buyer purchases. This engine reads your catalog, orders and customer groups, then works out which products to suggest to each customer and on each product page. Trade and retail buyers can see different suggestions. You grant an API account limited to the scopes it needs, and you decide which catalog fields it may update.
- Personal suggestions for each BigCommerce customer group, drawn from real orders
- Per-product recommendations that respect your categories and brands
- Tested against your own order history before anything goes live
$5,250
20–30 working days
Optional maintenance $265/mo
What it is
AI Recommendation Engine for BigCommerce uses a BigCommerce API account with scoped permissions to read your catalog, orders and customers, then computes personalized and per-product recommendations. Output arrives as a recurring feed or dashboard for your storefront and email, with up to five alerts, and agreed catalog fields can be written back. Suggestions are tested on your history first.
Why businesses buy it
Generic related-product blocks show the same items to everyone, so trade buyers see retail picks and loyal customers see products they already own. That quietly shrinks basket size on every visit. This engine learns from your own BigCommerce orders, so suggestions follow what similar buyers actually purchased, and your team stops hand-picking related products. The written scope tells you before you order which data it reads, which fields it may write, how it is tested and what the feed looks like, so there is no open-ended project to manage.
Problems this solves
Large BigCommerce catalogs leave good products buried several pages deep
A scoped BigCommerce API account lets the engine read your catalog, orders and customers. It computes personalized and per-product suggestions, so deeper catalog items can surface next to what shoppers already view.
Recommendation tools often ask for broad store permissions
The API account is scoped to the permissions the agreed field mapping needs. Keys stay with you and are never sent by email, and every written-back catalog field is documented.
Changes in what sells together go unnoticed until a season ends
Up to five alerts flag movement in the metrics you define. Suggestions are tested against three months of your order history first, so you can judge how they behave before launch.
A good fit if
Your store is on BigCommerce, with a catalog organized by categories and brands that your current suggestions ignore.
You serve distinct customer groups, such as trade and retail buyers, who should not see the same suggestions.
You prefer to grant an API account limited to the scopes this build needs, rather than full store access.
You have at least three months of BigCommerce orders, and someone who can join a 30-minute setup call.
Not the right fit if
Customers mostly ask where their order is, rather than what to buy. See AI Order Support Agent for BigCommerce.
Your product descriptions need writing before recommendations make sense. See AI Product Description Engine for BigCommerce.
You want carts recovered after shoppers leave. See AI Abandoned Cart Agent for BigCommerce.
Your platform is Adobe Commerce. See AI Recommendation Engine for Adobe Commerce.
How it works
- Your BigCommerce admin creates an API account with the scopes we list: read access to orders and customers, and write access only to the catalog fields agreed on a 30-minute call.
- The engine reads three months or more of orders together with your catalog structure, so categories and brands shape which suggestions make sense.
- Customers can be segmented, for example by customer group, so each segment gets suggestions drawn from what similar buyers purchased.
- Browsing behavior, where you have it, comes from the one extra system and is combined with purchase data.
- The model is checked against three months of your order history, and the comparison is shared with you before launch.
- Recommendations go out as a recurring feed or dashboard on your chosen channel, with up to five alerts and agreed catalog fields updated in BigCommerce.
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.
- A BigCommerce API account with the scoped permissions we specify.
- Your BigCommerce admin for a 30-minute setup call.
- Three months or more of order history in BigCommerce.
- Your metric definitions, including any customer segments that matter.
- Catalog data, plus your shipping, returns and refund policies.
- Read access to a behavior source if you have one, such as your analytics platform.
- Report recipients, the delivery channel, and one person who approves decisions.
- 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; API account scopes and segments agreed.
Weeks 1–2
Model built on catalog structure, orders and customer segments.
Week 3
Feed, alerts and catalog write-back connected.
Weeks 3–4
Three-month history check completed and shared.
Week 4
Review with you, then two rounds of changes.
Go-live
Recommendations live for your BigCommerce store, handoff recorded.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $5,250 |
| Express | 15–25 working days | $6,050 |
| Rush | 10–20 working days | $6,850 |
What you receive at handover
- AI Recommendation Engine for BigCommerce configured and deployed
- A scoped BigCommerce API connection with documented field mapping
- A recurring feed or dashboard for storefront and email, with up to five alerts
- A test report on sandbox or test records, checked against three months of order history
- An admin guide, a recorded 60-minute handoff session and 30 days of post-launch support
Expected benefits and how to measure them
- Can help more of a large catalog reach the shoppers likely to want it
- Helps keep store permissions narrow and on record
- Makes shifts in buying patterns easier to catch early
- Product page add-to-cart rate
- Compare it in BigCommerce Analytics or your web analytics for the four weeks before and after launch.
- Average order value
- Read it from the BigCommerce orders report for matching periods, avoiding promotional weeks.
- Share of catalog receiving orders
- Count products with at least one sale each month from the BigCommerce product report and compare against the pre-launch month.
Before you start: Record last month's average order value and add-to-cart rate. Also count how many products sold at least once.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
The build price is paid once. Running costs are roughly $20 to $150 a month for AI model usage and the scheduler that refreshes recommendations, paid in your own accounts. Your BigCommerce plan stays as it is; licenses and upgrades are excluded.
AI usage while building and testing is billed to your accounts directly.
Ask about running costsCustom requirements
Separate models per storefront, a second delivery channel, a third data source, or custom BigCommerce objects need a written Change Quote. Note it at checkout and the quote follows.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes and a monthly allowance of change hours, such as a new customer segment or retraining after a range change.
$265/mo (5% of the build price a month, 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 recommendation app for BigCommerce bills monthly by traffic or orders | A data agency quotes a project fee | Ours is one fixed price, shown before you buy |
| Access | Often asks for broad store permissions | Agreed during the project | Ours uses an API account scoped to what the build needs |
| Segments | One recommendation list for every shopper | Depends on the brief | Ours can tailor suggestions by customer segment |
| Proof | Judged after launch | A report at the end | Ours is checked against three months of your orders first |
Questions about this product
What BigCommerce permissions does the recommendation engine need?
An API account with scoped permissions: read access to orders, customers and the catalog, and write access only to the catalog fields agreed in the mapping. Your admin creates the account and can review or remove it at any time. Custom BigCommerce objects are not included in this build.
Can AI Recommendation Engine for BigCommerce treat customer groups differently?
Yes. Customer segments, such as groups you already keep in BigCommerce, can be defined as part of your metrics, and each segment's recommendations are drawn from what similar buyers purchased. Separate models per storefront sit outside the base scope and need a Change Quote.
Does the BigCommerce edition respect our categories and brands?
It uses them. Your catalog structure, including categories and brands, is read alongside orders, and rules you set, such as never suggesting across certain categories, are applied when recommendation sets are built. Those rules are tested in the three-month history check before launch.
What is included in the BigCommerce recommendation build?
One fixed price covers the scoped API account setup, the field mapping for catalog write-back, the recommendation model, a test against three months of your order history, two rounds of changes, a recorded handoff and thirty days of support. Faster delivery options are shown in the delivery panel. The clock starts once your API account, metric definitions and order access are verified.
What happens after the BigCommerce engine launches?
The feed keeps refreshing on its schedule, and up to five alerts flag shifts, such as a recommended product's sales dropping. Thirty days of support follow launch. After that, optional monthly maintenance covers retraining and new metrics within its monthly change hours.
What are the monthly running costs on BigCommerce?
Roughly $20 to $150 a month in your own accounts for AI model usage and the scheduler that refreshes recommendations. Your BigCommerce subscription is separate and does not change. There is no per-order or per-click fee from us, whatever your store's volume turns out to be.
Guides on this topic
All guides Suggest products that fit each kind of BigCommerce buyer.
Order AI Recommendation Engine for BigCommerce and your catalog, orders and customer groups produce a tested recommendation feed on a fixed delivery date, through an API account scoped to only what it needs.
Works well with
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