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.
Revenue
AI Recommendation Engine
Personalized product recommendations computed from real behavior and purchase history, delivered to feed your site and email.
Showing every shopper the same best-sellers leaves money on the table. This engine analyzes behavior and purchase history to compute personalized product recommendations for each customer, then delivers them as a recurring feed for your storefront widget and email platform. You choose where the recommendations appear, and reporting shows how they perform over time.
- Personalized recommendations from real behavior and purchases
- A recurring feed for your site and email platform
- Revenue and conversion reporting on every recommendation set
$4,500
20–30 working days
Optional maintenance $249/mo
What it is
AI Recommendation Engine analyses shopper behavior and purchase history across up to three read-only data sources to compute personalized product recommendation sets, delivered as a dashboard or recurring feed you plug into your storefront widget and email platform. You choose where the recommendations appear.
Why businesses buy it
Relevant recommendations are one of the most reliable ways to raise order value and repeat purchases, but most stores rely on generic best-seller blocks. Building real personalization usually means expensive platforms or a data team. This engine computes recommendations from your own data and hands you a feed your existing tools can use. The written scope sets out the data sources, the recommendation sets and the delivery format before you order.
Problems this solves
Every shopper sees the same bestsellers whatever they bought before
The engine reads purchase history and browsing behavior from up to three read-only sources. It computes a personalized recommendation set for each shopper, delivered as a feed your storefront widget can display.
Email campaigns push one product list to the whole customer base
The same recommendation sets can feed your email platform, so each send can carry products matched to that customer. You decide where recommendations appear, and nothing is forced onto a page.
Nobody can tell whether product suggestions help or just fill space
A dashboard or recurring report tracks up to 25 defined metrics, with up to five alerts for shifts. Before launch, results are checked against three months of your own historical data.
A good fit if
Your on-site 'you might also like' section and your post-purchase emails currently run on generic best-sellers instead of real customer behavior.
You have at least three months of order and browsing history you can give read-only access to.
You already have a storefront recommendation widget or an email platform ready to consume a computed recommendation feed.
You want a recurring, monitored recommendation output with alerts, not a one-time list you have to refresh manually.
Not the right fit if
You want collection-level curation decisions, not per-shopper personalization. See AI Merchandising Agent, which recommends collection order and featured products from aggregate sales data.
You want a live, conversational product finder shoppers talk to directly. See Shopify AI Sales Assistant or WooCommerce AI Sales Assistant built for guided selling during the visit.
Your catalog data itself is inconsistent, which will undermine any recommendation built on it. See AI Catalog Optimization, which finds and fixes catalog issues first.
You need more than three read-only data sources or more than five alerts, for example separate models across five customer segments. 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 orders, browsing behavior and your catalog, plus at least three months of history.
- The engine builds a per-shopper and per-segment recommendation model from real purchase and browsing patterns, not a fixed 'frequently bought together' rule.
- It checks the model against three months of your historical data to confirm the recommendations actually match past buying behavior before anything ships.
- Results are compiled into a dashboard and a recurring feed, delivered to the channel you chose, ready to plug into your storefront widget and email platform.
- Up to five alerts flag the moments worth a human look, such as a sudden shift in which products are driving repeat purchases.
- Your team, or your existing recommendation widget and email tool, applies the output on-site and in campaigns.
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 your order history, browsing behavior and catalog data sources.
- Your own definitions of the key metrics, such as what counts as a meaningful repeat-purchase signal.
- At least three months of historical order and browsing data, so the model reflects real behavior.
- The report recipients and the delivery channel you want, whether that is email, Slack or a dashboard feed.
- The name of the person who reviews the first recommendation set 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 order and behavior data confirmed.
Weeks 1–2
Metric definitions and the recommendation model built.
Week 3
Dashboard, feed 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 feed delivered, recorded handoff session, support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $4,500 |
| Express | 15–25 working days | $5,300 |
| Rush | 10–20 working days | $6,100 |
What you receive at handover
- The recommendation engine configured and running in your own environment
- Read-only connections to up to three data sources, plus the workflow, prompt and guardrail settings
- One dashboard or recurring feed with up to five alerts, on the delivery channel you chose
- A test report comparing results with three months of your historical data
- An admin guide and a recording of the 60-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can make product discovery feel more relevant to each returning shopper
- Helps storefront and email suggestions draw from one consistent source
- Makes it easier to see which recommendation placements earn their spot
- Recommendation click-through rate
- Compare clicks on recommendation widgets or email blocks in your analytics for the four weeks before launch and the four weeks after.
- Average order value
- Take it from your store platform's sales report for matching periods before and after launch. Leave out major sale weeks on both sides.
- Revenue from orders containing a recommended product
- Use your storefront or email platform's attribution report and total it each month to follow the trend.
Before you start: Record last month's average order value, items per order and any existing recommendation click rate. Note which pages and emails already show product suggestions.
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 model 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 separate models per customer segment 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 and a monthly allowance of change hours for things such as a new metric or a retrained segment. 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 recommendation widget charges a monthly fee tied to traffic volume | A data agency quotes a project fee for a custom model | Ours is one fixed price, shown before you buy |
| Scope | A widget recommends from a fixed 'bought together' rule | An agency agrees scope during the project | Ours is built around the metrics you define |
| Personalization depth | Most widgets ignore browsing behavior outside the current session | An agency build depends on what was agreed | Ours reads three months of real order and browsing history |
| Delivery | A widget installs in a day but the logic stays generic | An agency usually takes several weeks, set per project | Ours has a fixed delivery time shown before you buy, with faster options |
| Monitoring | Most widgets give 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 Recommendation Engine 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 Recommendation Engine cost to run each month?
Typically $20 to $150 a month, covering AI model usage and the scheduler that runs the recurring recommendation feed, billed to your own accounts. There is no separate platform fee from us. Optional monthly maintenance covers ongoing retraining. It also includes monitoring, fixes and change hours for a new metric or a retrained segment.
Does it place recommendations on my site directly?
It computes a personalized recommendation feed, delivered as a dashboard or recurring feed to your chosen channel. Placing it on your storefront or in email happens through your existing recommendation widget or email platform, which the feed is built to plug into.
How personalized are the recommendations, really?
They are built per shopper and per segment from real order and browsing history across up to three data sources, checked against three months of your own historical data, rather than a fixed 'customers also bought' rule applied the same way to everyone.
Can it feed both my website and my email platform?
Yes. The recurring feed is delivered to the one channel you set during intake and is built to plug into a storefront widget and an email platform at once, as long as both consume the same feed. Two entirely separate feeds count as a second delivery channel.
What happens once the AI Recommendation Engine's 30 days of support end?
You can move to monthly maintenance, which covers monitoring, fixes, updates and a monthly allowance of change hours. It is optional, bills monthly and can be cancelled in any month. Without it, the feed keeps running; you simply lose included changes.
Do I own the recommendation model once it is live?
Yes. The metric definitions, model logic and delivery schedule are built inside your own connected accounts, so nothing about ongoing output 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 Recommend from real behavior, not a guess.
Order the AI Recommendation Engine and get a personalized recommendation feed built from real behavior and purchases, ready for your site and your email campaigns.
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