★★★★★
I am genuinely impressed with the work the Checkout Loom team did on my AI and automation project. They are extremely professional, skilled, friendly and easy to work with, and I am delighted with the final result.
DavidUnited States
Verified
AI product recommendation engine for ecommerce
AI product recommendation engine for ecommerce that shows each shopper relevant products and proves the lift with a test
AI product recommendation engine for ecommerce work pays only when the suggestions are relevant, in stock and tested, because a block of best sellers shown to everyone teaches shoppers to ignore it, and a clever model that recommends sold-out or low-margin items does harm. We learn which products belong together from your orders and browsing, add rules for stock, margin and exclusions, serve real-time and personalized recommendations on product pages, carts and emails through your theme or an API, and run a controlled test so the order value lift is measured and not claimed.
- Fixed price from $2,540, paid once
- Only in-stock products, within your margin rules
- The lift is measured in a controlled test
- Platforms supportedShopify with Search and Discovery and the Storefront API, WooCommerce, BigCommerce and headless storefronts through a recommendation API.
- Typical storeStores with several hundred products and at least a few thousand orders, where shoppers often buy more than one item.
- PlacementsProduct pages, cart, post-purchase, search results and email blocks, each with its own rules.
- Storefronts coveredOne storefront per plan, with extra storefronts, languages and email platforms available as priced options.
- OutputsA trained model, a recommendation API or theme blocks, business rules, a test plan and a results report.
- Live inFive to seven weeks for the first placements and up to ten weeks for several placements with a full test programme.
The costly disconnect
Why most store recommendations are ignored, irrelevant or quietly harmful
A row of recommended products should lift order value, but on many stores it shows the same best sellers to everyone, includes items that are out of stock and has never been tested, so nobody knows whether it helps or whether shoppers simply scroll past it.
The same list for every shopper
A generic best-sellers row ignores what the shopper is looking at, so a buyer of a camera sees socks and a buyer of a coat sees the camera. It is easy to overlook until a busy week exposes it, and by then the cost to ecommerce is already counted in refunds, hours or lost sales in the product recommendation engine work.
Sold-out and low-margin items promoted
A model that learns only from clicks will push popular items regardless of stock or margin, which frustrates shoppers and weakens profit. The fix is not more effort but a better rule, and that is exactly what the product recommendation engine build for ecommerce puts in place.
No test, no evidence
Without a control group, an uplift in order value can be the season or a promotion, and a recommendation block that does nothing looks the same as one that works. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of product recommendation engine for ecommerce.
Cold start for new products
A new product has no orders to learn from, so a pure co-purchase model never recommends it until it is too late. Left alone, this problem repeats with every order, which is why product recommendation engine for ecommerce is built to handle it by rule and not by memory.
Privacy and consent problems
Personalising from browsing history needs care about consent, and rules differ by region, so a tool that tracks everyone by default carries risk. It is easy to overlook until a busy week exposes it, and by then the cost to ecommerce is already counted in refunds, hours or lost sales in the product recommendation engine work.
Slow and stale suggestions
Recommendations rebuilt monthly miss trends and new bundles, and real-time updates need a design that does not slow the page. The fix is not more effort but a better rule, and that is exactly what the product recommendation engine build for ecommerce puts in place.
- +8%order value lift is a typical result for tested recommendation blocks in our builds, which is indicative and varies by catalogue and traffic.
- 1control group of shoppers who see the old block is how we measure the real effect.
- 100%of recommendations are filtered for stock and your exclusion and margin rules before they are shown.
Sources: Shopify Search and Discovery and Storefront API product recommendation documentation; lift figures come from catalogues we have worked with and are indicative.
What changes on your store once recommendations are learned, filtered and tested
Each placement shows products that fit the shopper and the moment, within your rules, with a measured effect.
How recommendations work today
- Everyone sees the same best sellers.
- Sold-out items appear in recommendations.
- Margin and exclusions are ignored.
- New products are never recommended.
- Nobody knows whether the block works.
How they work afterwards
- Recommendations come from what similar shoppers bought together and viewed.
- Only in-stock, eligible products are shown.
- Margin, brand and exclusion rules shape the ranking.
- New products get exposure from content similarity until orders accumulate.
- A controlled test shows the lift in order value and add-on rate.
What your test report shows
- Higher order value.
- Fewer dead ends from sold-out items.
- Profit-aware suggestions.
- New products discovered sooner.
- Evidence for what works.
What the engine selects
- Learning product relationships from orders, carts and browsing.
- Selecting products for each placement in real time.
- Filtering for stock, exclusions and margin rules.
- Using content similarity for new products with no history.
- Reporting lift and add-on rate against a control group.
What a merchandiser decides
- Setting margin, brand and exclusion rules.
- Choosing placements and the number of items.
- Pinning or blocking specific recommendations.
- Deciding when a test result is conclusive.
- Promotional priorities.
Which online stores gain most from an AI product recommendation engine
If shoppers often buy more than one product, or your catalogue is too large to browse, recommendations are one of the most reliable ways to raise order value.
Stores with accessories and add-ons
Cameras, outdoor gear and furniture sell complements that recommendations surface at the right moment. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for product recommendation engine for ecommerce.
Stores with repeat customers
Past purchases guide what to suggest next, in the site and in emails. We look at this situation in the audit and give ecommerce an honest view of whether the build is worth doing now or later in the product recommendation engine work.
Stores with large catalogues
Shoppers cannot browse thousands of products, and recommendations narrow the choice. If this describes your business, the product recommendation engine work for ecommerce is likely to pay back quickly, and we will tell you plainly if it will not.
Stores with varied customer types
Personalised blocks serve beginners, enthusiasts and gift buyers differently. Owners in this position often say the biggest change is the time returned each week, and product recommendation engine is built to deliver that first for ecommerce.
Stores selling in several markets
Recommendations follow local stock, prices and popular combinations. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for product recommendation engine for ecommerce.
Stores with frequently-bought-together sets
A frequently bought together AI model finds bundles that shoppers already buy and presents them clearly. We look at this situation in the audit and give ecommerce an honest view of whether the build is worth doing now or later in the product recommendation engine work.
The behaviour events that train and trigger each recommendation
The engine learns from orders and browsing and responds to what the shopper is doing right now.
orders_syncedOrders are synced
Order lines update the co-purchase relationships, so bundles that shoppers really buy appear in recommendations. The event is logged with the rule that handled it, which lets you check later exactly what product recommendation engine did for ecommerce and why it did it.
product_viewedA product is viewed
The product and the session context select related and complementary items for the page. Because this trigger is handled separately from the others, a problem with it never delays the rest of the product recommendation engine work for ecommerce.
cart_updatedThe cart changes
The cart contents select add-ons that fit and avoid items already in it. You decide how much of this runs without approval, and for ecommerce we usually begin with a review step until the first results look right in the product recommendation engine work.
stock_changedStock changes
A sold-out product is removed from every placement, and a restocked one returns after a delay. Anything this trigger cannot decide safely is passed to you with the record attached, so product recommendation engine for ecommerce never acts on a doubtful reading.
product_addedA new product is added
The product is recommended by content similarity until it has enough orders to learn from. We test this moment on your own records before launch, so the product recommendation engine build for ecommerce has met it before it meets a real customer.
test_window_endedA test window ends
The lift in order value and add-on rate is compared with the control group and reported. The timing of this step is written down in the rule table, which means you can change when it happens without asking us to rebuild anything in the product recommendation engine build for ecommerce.
One shopper followed from a product page to a tested add-on purchase
This shows how a recommendation is chosen, filtered and measured for a single shopper.
- 01
The shopper views a camera
A visitor opens a mirrorless camera page, and the engine reads the product, the session and the market. If the step cannot finish, the record says why and waits for you, so product recommendation engine for ecommerce never moves on with a half-completed task.
- 02
Candidates are found
From orders, the engine finds that buyers of this camera often also buy a strap, a spare battery and a memory card. You can see the outcome of this step before the next one starts, which keeps the product recommendation engine build for ecommerce under your control throughout.
- 03
Rules are applied
The memory card with low stock is removed, a low-margin accessory is ranked lower and the camera already in the cart is excluded. The step uses only the facts you supplied, so nothing in the product recommendation engine result for ecommerce depends on guesses or on outside sources you did not approve.
- 04
Four items are shown
The strap, the battery, a case and a cleaning kit appear in a frequently bought together block, ordered by relevance and profit. Timing for this step is paced to the limits of your tools, so product recommendation engine for ecommerce keeps running calmly even when volume is high.
- 05
The shopper adds one
The shopper adds the case to the cart, and the cart block then suggests only items that are not already there. A check at the end of the step compares the result with the input, which catches the small mistakes that otherwise surface days later in the product recommendation engine build for ecommerce.
- 06
The test records the result
Because this shopper was in the test group, the order value and the add-on are compared with the control group in the weekly report. Because the steps are separate, a change to one of them, such as a new rule for ecommerce, does not disturb the others in the product recommendation engine flow.
Pricing
Price your AI product recommendation engine
Choose a plan, tell us your platform and the placements you want, and add any extras, and the total and live date update as you go.
What an AI product recommendation engine costs and what raises the price
Our fee is fixed and paid once, while hosting, any recommendation app and model usage are billed by their own providers.
Catalogue size and orders
Larger catalogues and more orders mean more training and testing. The tools around product recommendation engine bill separately, and we estimate their monthly cost for this factor so your running cost is known as well as the build for ecommerce.
Placements
Each placement, such as product page, cart, search and email, adds a block and tests. Owners who run product recommendation engine for ecommerce often find this factor is the one that most changes the quote, which is why we ask about it first.
Markets and languages
Each market adds stock, price and ranking rules. We check this factor against your real records during the audit, so the number in the quote comes from your data and not from a guess in the product recommendation engine build for ecommerce.
Consent and tracking set-up
Consent-aware event collection adds build time, and this is one of the factors we price in writing before work starts, so the product recommendation engine quote for ecommerce reflects your real situation and not an average.
Test design
Several simultaneous tests and a longer window add reporting work. If this factor is small for you, it adds little to the price, and the written quote shows exactly how much it did add for ecommerce in the product recommendation engine work.
Real-time requirements
Fast real-time recommendations need caching and a hosted service. You can lower the cost by tidying this part first, and we say so plainly in the plan when the product recommendation engine work for ecommerce would benefit.
Hosting, recommendation apps and model usage are billed by those providers.
Next step
Ready to price AI Product Recommendation Engine for Ecommerce?
An AI product recommendation engine for ecommerce learns which products belong together from your orders and behaviour, serves personalized and real-time recommendations on product pages, carts and emails through your theme or an API, applies business rules for stock and margin, and proves its value with a controlled test.
- One fixed price from $2,540, paid once
- Only in-stock products within your rules
- The lift measured in a controlled test
Three more uses of the recommendation engine
The same model and rules can serve more placements than the product page.
Recommendations in email
Post-purchase and win-back emails include products chosen for each customer from their orders and the engine’s relationships. Results from this job appear in the same weekly report as the main product recommendation engine work, giving ecommerce one place to judge whether it earns its keep.
Search and category sorting
Search results and category pages can be sorted by relevance to the shopper and by profit within your rules. We include this idea because owners who run product recommendation engine for ecommerce often ask for it within a few months, and it is cheaper to plan for it early.
Bundles built from real baskets
Frequently bought together sets are found in real orders and offered as a bundle with a clear saving. The job follows the same approval rules as everything else, so nothing reaches a customer of ecommerce without passing the checks you already trust in the product recommendation engine work.
Which data the recommendation engine uses and where each fact is the source of truth
A recommendation depends on a few data sources, so we state which signals are used and which are never touched.
| Store object | Fields we use | Direction | Source of truth |
|---|---|---|---|
| Orders | Order lines, dates, markets | Read | Store orders |
| Browsing events | Views, cart adds, session id | Collected with consent | Storefront event stream |
| Catalogue | Title, category, attributes, price, margin class | Read | Store catalogue |
| Stock | Quantity by variant and market | Read before showing | Store inventory |
| Rules | Exclusions, margin weights, pins | Read before every response | Rule sheet |
| Test results | Group, orders, order value, add-ons | Written daily | Experiment log |
Browsing signals are collected only where consent allows, and the engine works from orders alone for shoppers who decline tracking.
Edge cases in recommendation engines that we test before launch
Recommendations misbehave at the edges, so we test them with deliberate cases before a single shopper sees them.
Recommending what is already in the cart
Items in the cart or already bought are excluded, so the block never repeats itself. We list the cases that matter most for product recommendation engine in your handover guide, so anyone on your team can understand how ecommerce deals with them.
New products and cold start
A new product is recommended from content similarity and then handed to the order-based model as sales arrive. A rule for this case can be tightened or relaxed in minutes in the rule table, without waiting for a rebuild or a new quote in the product recommendation engine build for ecommerce.
Sensitive or restricted products
Products you exclude, such as age-restricted goods, never appear in recommendations for other products. Handling the case early protects the margin and the reputation of ecommerce, which is worth more than the hours it takes to write the rule in the product recommendation engine work.
Popularity bias
A diversity rule stops the engine from recommending only the biggest sellers and ignoring good niche products. We test this case before launch on your own records, so product recommendation engine for ecommerce has met it before it meets a real customer on a busy day.
Different markets and currencies
Recommendations follow the market’s stock, prices and popular combinations. When the case appears, the workflow follows a written rule and records the choice it made, so you can review it afterwards if you wish in the product recommendation engine build for ecommerce.
Slow pages
Blocks load after the main content with a time limit, so a slow recommendation never delays checkout. If a rule cannot settle the case safely, it goes to you with the record attached, and product recommendation engine for ecommerce waits instead of guessing.
Best-seller blocks, a recommendation app and a built recommendation engine compared
A best-seller block is simple and generic, a recommendation app such as Nosto, Rebuy or LimeSpot is quick to install, and a built engine fits the model, the rules and the tests to your catalogue and margins.
| Best-seller block | Recommendation app | Checkout Loom build | |
|---|---|---|---|
| Personalised to the shopper | – | ✓ | ✓ |
| Stock and margin rules you control | Partly | Partly | ✓ |
| Cold start for new products | Partly | Partly | ✓ |
| A controlled test with a report | – | Partly | ✓ |
| A recommendation API for any placement | – | Partly | ✓ |
| Cost pattern | Free | Monthly plan by traffic or orders | Our build fee plus hosting and model costs |
Shopify’s free Search and Discovery app offers related and complementary recommendations and is a good baseline, and we measure our build against it in the test.
How the recommendation engine is built and what each part costs
We choose the model and the delivery route for your traffic and show the monthly cost of each part beside our fixed fee.
Co-purchase and similarity models
Item-to-item relationships from orders, content embeddings from titles and attributes and a ranking layer that blends them. Everything this layer does for product recommendation engine is logged, so you can see what ran, when it ran and what it cost on any given week for ecommerce.
A recommendation API
A small, fast service that returns ranked product ids for a placement, with caching and a time limit, usable by any storefront or email tool. We test the layer on your own data before launch, so product recommendation engine for ecommerce is proven on real records and not on tidy examples.
Theme blocks
Shopify theme sections or WooCommerce blocks that call the API and render the recommendations in your design. If the layer fails or changes its terms, the handover guide explains how to swap it, so ecommerce is never stranded by one supplier in the product recommendation engine work.
A rules layer
Stock, exclusion, margin and diversity rules applied to every response. Its role is written in plain language in the handover guide, which means a new team member at ecommerce can understand it in a few minutes in the product recommendation engine work.
An experiment framework
A fair split of shoppers into test and control groups with a log of orders and order value for each. The monthly price of this layer is written next to our fixed fee before you commit, so the true cost of running product recommendation engine for ecommerce is clear from day one.
Make, Zapier or n8n
The workflow layer that refreshes the model, syncs data and feeds emails. You can replace this layer later with another tool, because the rules live in a table you own and are not locked inside any one product in the product recommendation engine build for ecommerce.
How we keep recommendations fast, relevant and safe
A recommendation block must never slow checkout or show a wrong product, so speed and filtering are built in.
A strict time limit
The API answers within a short limit, and a slower response falls back to a cached or rules-based list. Reports on how often the safeguard fires show ecommerce whether the product recommendation engine work is getting steadier or whether a rule needs attention.
Filters before display
Stock, exclusions and eligibility are checked at response time, not only when the model is trained. Because this protection is written as a rule and not as a habit, it keeps working when staff change or the business gets busier in the product recommendation engine build for ecommerce.
Scheduled retraining
The model refreshes on a schedule and when large catalogue changes occur. When something goes wrong in product recommendation engine for ecommerce, the failure is named, retried within safe limits and listed for you, instead of vanishing into a log nobody reads.
Alerts that name the placement
Each alert gives the placement, the response time and the cause. This safeguard runs on every batch and not only on a schedule, so a problem in product recommendation engine is caught before it can spread to the next record for ecommerce.
A control group always on
A small group keeps seeing the old block, so the lift is always measured against something real. Each alert names the rule, the record and the next step, which means you can fix the cause from the message alone without opening any tool in the product recommendation engine build for ecommerce.
Plain explanations after problems
If a bad recommendation reaches shoppers, the owner explains in writing what happened and the rule that now prevents it. We compare the results of product recommendation engine with your own records on a regular basis, so a small drift for ecommerce is repaired before it becomes a large one.
Privacy, consent and permissions in a recommendation engine
Personalisation uses behaviour data, so we collect only what consent allows and keep identifiers minimal.
Least-privilege access
We request read access to orders, products and inventory and no payment or address data. For product recommendation engine on ecommerce, access is limited to what the job needs and can be withdrawn in a minute, so ending the work leaves nothing behind.
Consent-aware tracking
Browsing signals are collected only where consent allows, and non-consented shoppers receive order-based recommendations. Nothing in this arrangement requires you to share a password, because access is granted from your own account in a form you can revoke in the product recommendation engine build for ecommerce.
Keys in your accounts
Store and hosting keys sit in your accounts and can be revoked at any time. We keep no copy of your keys, records or customer details in systems that belong to us, and the handover guide shows you how to confirm that in the product recommendation engine build for ecommerce.
Anonymous identifiers
Sessions use anonymous identifiers, and we do not combine them with personal data in the engine. Only the fields that product recommendation engine needs are read, and only for as long as the task takes, which keeps the exposure for ecommerce as small as it can be.
Email recommendations respect consent
Recommendations appear only in emails sent to customers who agreed to marketing. Every access is logged in your own accounts, so you can see who or what touched a record and when, without asking us for a report in the product recommendation engine build for ecommerce.
Handover and removal
You receive a list of every service, block and key we added, and our own access is removed on the last day. If a key is ever lost, it can be replaced in minutes without rebuilding the product recommendation engine work for ecommerce, because the rules do not depend on it.
What you receive with your AI product recommendation engine
Each plan ends with working placements, rules you control and measured results.
- A trained recommendation model built from your orders and catalogue.
- A recommendation API or theme blocks for the placements in your plan.
- Rules for stock, exclusions, margin and diversity.
- Frequently bought together sets found in real baskets.
- Content-based recommendations for new products.
- A controlled test with a fair split and a decision rule.
- A results report on order value, add-on rate and revenue per visitor.
- Email blocks for the customers who agreed to marketing, where included.
- A month of corrections after the placements go live.
Verified client reviews and the measurement behind our recommendation builds
The unedited words of verified clients of the Checkout Loom team appear below, followed by the measurement rules every recommendation build follows.
- 1control group stays on the old block so the lift is always measured.
- 100%of recommendations are filtered for stock and your rules before display.
- 30 daysof corrections follow launch so the engine settles before we step back.
Measurement rules follow standard experiment design, and platform behaviour follows Shopify and WooCommerce documentation.
★★★★★
The Checkout Loom team built an automation that simplified my workflow and worked perfectly from day one. Communication was excellent, delivery was fast, and I would happily recommend the service.
RebeccaUnited States
Verified
★★★★★
They set up my automation remarkably fast and executed everything perfectly. Communication and service were top tier, so I could not be happier with the results and I highly recommend the Checkout Loom team.
AbbieGermany
Verified
★★★★★
The automation was set up in a clean and practical way by the Checkout Loom team. It saved me a lot of manual work and made the whole process much easier.
JanoUnited Kingdom
Verified
★★★★★
I highly recommend ordering from the Checkout Loom team. I had slow responses and poor communication with other providers, but this team was great and I am very happy with their service.
HarmetUnited States
Verified
★★★★★
I am a returning customer because my first AI automation workflow worked so well. Once again everything was built exactly as requested, and the whole process was reliable from start to finish.
WilliamUnited States
Verified
★★★★★
The determination and commitment to quality from the Checkout Loom team were clear from day one. They understood my requirements perfectly and completed everything with professionalism and care, and it is rare to find a team this dependable.
LorcanColombia
Verified
★★★★★
It was an excellent experience from start to finish, and the automation works reliably with everything configured properly. It is clear that the team has strong knowledge of automation, and I will definitely be returning for future work.
EllieUnited States
Verified
★★★★★
The team carefully investigated the problem, tested different scenarios and delivered a dependable solution. A duplicate contact issue had been causing problems for quite some time, and it is finally resolved.
RichardUnited States
Verified
How the recommendation build runs from order analysis to a measured rollout
We learn from your orders first, because they show which products really belong together.
- 01
Analyse orders and the catalogue
We study baskets, repeat purchases and catalogue structure and find the relationships worth recommending. At the end of this step you approve or ask for changes, and the next stage of product recommendation engine starts only after your reply for ecommerce.
- 02
Write the rules
We agree stock, margin, exclusion and diversity rules with you in writing. The time this step takes depends on how quickly your records and answers arrive, and we tell ecommerce early what we need to keep moving in the product recommendation engine work.
- 03
Build the model and API
We build the models, the ranking and the recommendation API with caching and a time limit. We use plain language in every document from this step onward, so anyone at ecommerce can read the plan and understand it in the product recommendation engine work.
- 04
Add the placements
We add blocks to product pages, the cart and emails in your theme, tested for speed. A short summary of the step is added to the handover guide, so the way product recommendation engine was built for ecommerce is never lost.
- 05
Run the test
We split shoppers fairly, run the test for the planned period and read the result with you. You see the result of this step in writing before the next one begins, which keeps the product recommendation engine build for ecommerce on schedule and under your control.
- 06
Roll out and hand over
We roll out the winning setup and hand over the guide and the report with a month of corrections. If this step shows a problem, we say so at once and propose the fix, and the date for product recommendation engine is moved in writing rather than quietly for ecommerce.
Estimate what tested recommendations could add to your revenue
Enter your orders and order value and the lift you expect and the estimate shows the extra revenue from a modest lift in order value.
Extra revenue each month–
Extra revenue each year–
Average order value afterwards–
The lift applies only to orders that see a recommendation block, and a measured test will give you your own figure.
AI product recommendation engine questions, answered in full
These are the questions store owners ask about personalisation, software, APIs, testing and cost before they order a recommendation engine.
What is an AI product recommendation engine for ecommerce?
It is a system that learns from your orders and browsing which products belong together or suit a shopper, serves those suggestions on product pages, carts and emails, applies rules for stock and margin and measures the effect on order value in a controlled test.
How is an ecommerce recommendation engine different from a best-seller block?
An ecommerce recommendation engine chooses products to fit what the shopper is viewing or has bought, whereas a best-seller block shows the same items to everyone, so the engine tends to be more relevant and lifts add-on sales more.
What are AI product recommendations based on?
AI product recommendations use co-purchase patterns in your orders, product content such as titles and attributes and, where consent allows, browsing behaviour, and they are filtered for stock, exclusions and margin before display.
Can you do personalized product recommendations?
Yes, personalized product recommendations for returning shoppers use their past orders and views where consent allows, and shoppers who decline tracking see order-based recommendations that need no personal data.
Should I buy product recommendation software or have one built?
Product recommendation software is quick to install and sensible for many stores, and a built engine suits stores that need their own margin and stock rules, an API for any placement and a controlled test. We compare both for your traffic and say which is cheaper over time.
What is a shopping recommendation engine for a small catalogue?
A shopping recommendation engine for a small catalogue relies more on rules and content similarity than on order history, because there is too little data for co-purchase patterns, and it improves as orders accumulate.
Can recommendations update in real time?
Yes, real-time product recommendations respond to the product being viewed and the cart contents within a strict time limit, with cached and rules-based fallbacks so the page is never slowed.
How does frequently bought together AI work?
Frequently bought together AI finds products that appear in the same orders more often than chance would predict, ranks them by relevance and profit, removes items that are sold out and presents the best few as a set or a block.
What is a cross-sell recommendation engine and what is an ecommerce personalization engine?
A cross-sell recommendation engine suggests complementary products at the product page, cart and post-purchase, and an ecommerce personalization engine extends the same idea to search, category order and email for each shopper. A recommendation API for ecommerce lets any of these placements call the same model.
How do you prove the engine works?
A fair share of shoppers keeps seeing the old block, and we compare order value, add-on rate and revenue per visitor between the groups over a planned window, so the lift is measured in your own store.
How soon will recommendations be on my store and what do you need to start?
The first placements are usually live in five to seven weeks, followed by the test. We need an export of your orders, access to your store and theme, your margin and exclusion rules and consent settings, and we send a written checklist after you order.
Show every shopper products that fit, and prove that it raised your order value
Choose a plan in the pricing calculator and continue to checkout, or request a quote with your store address, the number of orders you take each month and the placements you want. We reply in writing within one working day with a confirmed start date and a short checklist, so the order analysis begins at once.
- One fixed price from $2,540, paid once
- Only in-stock products within your rules
- The lift measured in a controlled test