Product recommendations
built for fashion.
You pay only for results.
Product recommendations
built for fashion.
You pay only for results.
Bytem AI is a product recommendation engine for fashion e-commerce. We A/B test it against your current recommendations for free. You pay only after you see the lift in your own analytics.
Bytem AI is a product recommendation engine for fashion e-commerce. We A/B test it against your current recommendations for free. You pay only after you see the lift in your own analytics.
Revenue Lift
29.98%
A US fashion store
7-week A/B test
Source:
store's own analytics
W1
W2
W3
W4
W5
W6
W7
Revenue Lift
29.98%
A US fashion store
7-week A/B test
Source:
store’s own analytics
W1
W2
W3
W4
W5
W6
W7
Revenue Lift
29.98%
A US fashion store
7-week A/B test
Source:
store's own analytics
W1
W2
W3
W4
W5
W6
W7
Screenshots from client Slack channels and email threads. Names redacted.
Screenshots from client Slack channels and email threads. Names redacted.
Everyone else sells you features. We sell you results.
Most recommendation platforms sell you a stack of features. We'd rather be judged on results you can check yourself.
Recommendations with real lift
Merchandising that hits your targets
Results you can audit
Recommendations with real lift
Most recommendation widgets were built to sell iPads, vacuums and dog food — not dresses.
A shopper is looking at a USB-C charger, and you show her other USB-C chargers. That logic works. But when she's drawn to a dress because it makes her feel sexy and ready to go out, and you show her a sweet, ladylike dress just because both are tagged "nylon" and "dress" — that's what the "You may also like" feature gets you. It hasn't lifted your sales at all.
It's not a one-off: "Frequently bought together" works for functional products — buy an iPad, and a case is the obvious add-on. But fashion pairs by look, not function. "Recommended for you" barely works on new arrivals with no click history, and every season brings a wave of new styles. And once she clicks a floral dress, she sees nothing but floral dresses until she's bored of them.
Recommendations with real lift
Merchandising that hits your targets
Results you can audit
Recommendations with real lift
Most recommendation widgets were built to sell iPads, vacuums and dog food — not dresses.
A shopper is looking at a USB-C charger, and you show her other USB-C chargers. That logic works. But when she's drawn to a dress because it makes her feel sexy and ready to go out, and you show her a sweet, ladylike dress just because both are tagged "nylon" and "dress" — that's what the "You may also like" feature gets you. It hasn't lifted your sales at all.
It's not a one-off: "Frequently bought together" works for functional products — buy an iPad, and a case is the obvious add-on. But fashion pairs by look, not function. "Recommended for you" barely works on new arrivals with no click history, and every season brings a wave of new styles. And once she clicks a floral dress, she sees nothing but floral dresses until she's bored of them.
So what actually makes recommendations sell more?
A model that reads fashion, not just clicks. A model tuned to your store, not shipped with defaults. And a fashion expert who owns the result after launch.
Built for fashion only
Agentic AI, run by experts
Tuned until it lifts
Built for fashion only
One model for iPads and dresses? Not ours.
One model rarely works for every category. Fashion has seasons; electronics barely do. In fashion, the product photo can make the sale. For most functional products, it hardly matters.
So we gave up every other category and trained a model just for fashion shoppers. It reads a garment's style and vibe, so it recommends pieces with the same look — not pieces that do the same job. It also knows how fashion shoppers browse, so it knows when to personalize and when to show similar styles or pieces to pair.
Each of these is a "feature" that has held up across hundreds of fashion stores. Actually, we call them model weights and strategies. We don't sell features.
Built for fashion only
Agentic AI, run by experts
Tuned until it lifts
Built for fashion only
One model for iPads and dresses? Not ours.
One model rarely works for every category. Fashion has seasons; electronics barely do. In fashion, the product photo can make the sale. For most functional products, it hardly matters.
So we gave up every other category and trained a model just for fashion shoppers. It reads a garment's style and vibe, so it recommends pieces with the same look — not pieces that do the same job. It also knows how fashion shoppers browse, so it knows when to personalize and when to show similar styles or pieces to pair.
Each of these is a "feature" that has held up across hundreds of fashion stores. Actually, we call them model weights and strategies. We don't sell features.
Works with the stack you already have
Bytem AI sits on top of your store. You keep your theme, your checkout, your analytics. Nothing about how you run the site changes.
Works with the stack you already have
Bytem AI sits on top of your store. You keep your theme, your checkout, your analytics. Nothing about how you run the site changes.
✔
Works with Shopify Plus, Wix, Magento and more.
✔
One API puts recommendations on any page in your website or app.
✔
Also in your email, SMS and push.
01
Add one script
One JS snippet in your site's <head>. We match the widgets to your theme, and nothing goes live until you approve it.
02
Send us your product feed
We use the product feed you already send to Google or Meta. No new data work for your team.
03
Read the report
The A/B test starts the day we go live. When the test ends, usually 14 to 60 days in, you get the report: what we added.
01
Add one script
One JS snippet in your site's <head>. We match the widgets to your theme, and nothing goes live until you approve it.
02
Send us your product feed
We use the product feed you already send to Google or Meta. No new data work for your team.
03
Read the report
The A/B test starts the day we go live. When the test ends, usually 14 to 60 days in, you get the report: what we added.
01
Add one script
One JS snippet in your site's <head>. We match the widgets to your theme, and nothing goes live until you approve it.
02
Send us your product feed
We use the product feed you already send to Google or Meta. No new data work for your team.
03
Read the report
The A/B test starts the day we go live. When the test ends, usually 14 to 60 days in, you get the report: what we added.
Free to test. Priced to your store.
Every plan comes with the same model and the same fashion e-commerce expert. Only the way you pay changes.
Free A/B test
Step 1 · Everyone starts here
Monthly plan
Step 2 · Most clients
Custom
For large or complex stores
Free A/B test
$0
/test
We run our recommendations against yours on the metric you choose. You decide whether it passed.
About 14 to 60 days on live traffic
You pick the metric to beat
Read the result in your own analytics
Nothing to pay during the test
For fashion stores with 500+ SKUs
Free to test. Priced to your store.
Every plan comes with the same model and the same fashion e-commerce expert. Only the way you pay changes.
Free A/B test
Step 1 · Everyone starts here
Monthly plan
Step 2 · Most clients
Custom
For large or complex stores
Free A/B test
$0
/test
We run our recommendations against yours on the metric you choose. You decide whether it passed.
About 14 to 60 days on live traffic
You pick the metric to beat
Read the result in your own analytics
Nothing to pay during the test
For fashion stores with 500+ SKUs
Free to test. Priced to your store.
Every plan comes with the same model and the same fashion e-commerce expert. Only the way you pay changes.
Free A/B test
Step 1 · Everyone starts here
Monthly plan
Step 2 · Most clients
Custom
For large or complex stores
Free A/B test
$0
/test
We run our recommendations against yours on the metric you choose. You decide whether it passed.
About 14 to 60 days on live traffic
You pick the metric to beat
Read the result in your own analytics
Nothing to pay during the test
For fashion stores with 500+ SKUs
Case studies
A/B tests on real fashion stores: what we changed, how we measured it, and what we found. One of them showed no lift, and it's here too.
2020
Jollychic
When the budget was cut everywhere, the recommendations stayed.
2020
Jollychic
When the budget was cut everywhere, the recommendations stayed.
2024
Shopmicas
How many impressions should a new arrival get? They had been setting that number by gut.
2024
Shopmicas
How many impressions should a new arrival get? They had been setting that number by gut.
2022
Chicwish
A new-arrival rule that was quietly costing 5% of revenue.
2022
Chicwish
A new-arrival rule that was quietly costing 5% of revenue.
2025
A printed-shirt store
A test with no lift, and why we told them not to buy.
2025
A printed-shirt store
A test with no lift, and why we told them not to buy.
My CEO called and asked what Bytem was actually adding. I said about 20%. He asked if I was sure, so I sent him the A/B report. We have not looked at another recommendation vendor since — this is year five.
Leon Carter
Director of E-commerce at ChicPoint
I have never had to explain product lifecycle or MOQ to a recommendation vendor before. They came in with a formula for how many impressions a new arrival should get on day one — I had been setting that number by gut for six years. Then the A/B test told me what it was worth.
Olivia Smith
Site Merchandising at MARKAVIP
It has been a tight year. We cut Facebook spend by 60% and dropped most of the software we thought we needed. Bytem was never on that list — the A/B report puts it in the revenue column, not the cost column.
Aaron Lee
CEO & Founder at Jollychic
How do you know the lift is real and not just a good month?
Because we do not ask you to trust a number calculated by a vendor-defined attribution. We ask you to run a randomized controlled trial. From day one, your traffic is split at random: one group sees Bytem, the other sees what you had before. Same season, same promotions, same viral moment. The only variable is the algorithm. That is the only thing we take credit for. You agree on the metric and the split before the test starts, and you verify it in your own analytics.
Can I check the results in my own analytics?
Yes. We can push the test data into your Google Analytics, so the result sits next to the rest of your reports. If you already have your own tracking, you can measure the test with it yourself.
How is this different from the recommendation app already on my store?
Two things. Most apps are built to work for every category, which means they are tuned for none. We build for fashion only, because fashion shoppers behave differently: style is subjective where function is objective, seasons matter, and the product image carries much of the decision. The second difference is what you get at the end. An app hands you a dashboard. We hand you a controlled test result. If you want to know which of us is better on your store, the honest answer is that we should run the test.
What data do you need, and where does it live?
Two things: a lightweight pixel on your storefront that records browsing and purchase events, and your product feed. We do not need customer names, email addresses or payment details. Your data is never mixed into another client's model.
How do you know the lift is real and not just a good month?
Because we do not ask you to trust a number calculated by a vendor-defined attribution. We ask you to run a randomized controlled trial. From day one, your traffic is split at random: one group sees Bytem, the other sees what you had before. Same season, same promotions, same viral moment. The only variable is the algorithm. That is the only thing we take credit for. You agree on the metric and the split before the test starts, and you verify it in your own analytics.
Can I check the results in my own analytics?
Yes. We can push the test data into your Google Analytics, so the result sits next to the rest of your reports. If you already have your own tracking, you can measure the test with it yourself.
How is this different from the recommendation app already on my store?
Two things. Most apps are built to work for every category, which means they are tuned for none. We build for fashion only, because fashion shoppers behave differently: style is subjective where function is objective, seasons matter, and the product image carries much of the decision. The second difference is what you get at the end. An app hands you a dashboard. We hand you a controlled test result. If you want to know which of us is better on your store, the honest answer is that we should run the test.
What data do you need, and where does it live?
Two things: a lightweight pixel on your storefront that records browsing and purchase events, and your product feed. We do not need customer names, email addresses or payment details. Your data is never mixed into another client's model.
Let's test it on your store.
Tell us your store URL and what you'd like to improve. The test is free, and you decide whether it passed.
Let's test it on your store.
Tell us your store URL and what you'd like to improve. The test is free, and you decide whether it passed.
hello@bytem.ai
Let's test it on your store.
Tell us your store URL and what you'd like to improve. The test is free, and you decide whether it passed.