AI Integration & Development · The regular
Show every customer what they came for
Recommendation and personalization engines that read each visitor's signals and surface the products, content, and offers they're most likely to act on, lifting conversion, order value, and retention.
Walk-in, 14:22
4th visit this month
What the shop noticed
+24%
Add-to-cart, typically
Real-time
Served inline
Day one
Works cold-start
A/B
Proven vs control
The brief
Most visitors leave because they didn't find it fast enough
You have what they want. It's just three pages deep. Personalization brings it to the surface: it reads what each visitor is drawn to and reorders the experience around them, so the right product, article, or offer is the first thing they see instead of the thing they never reach.
Done well, it's invisible. It just feels like your product finally gets them.
Relevant from the first click
It works before someone has a long history, using context as well as past behavior.
Learns as it goes
Every view, click, and purchase sharpens what each person sees next.
Lifts the metric that matters
Tuned to your goal, whether that's conversion, order value, or retention. Vanity engagement doesn't count.
The counter
Every surface a customer touches
Product recommendations
“You might like” and “frequently bought” that actually convert, across the whole store.
Content & feeds
Surface the articles, videos, or listings each visitor is most likely to engage with.
Search ranking
Order search and category results around what this specific person tends to want.
Email & lifecycle
Personalized product picks and timing in every send, instead of one blast for everyone.
Next-best-action
Recommend the right offer, plan, or step for each user at the right moment.
Offers & pricing
Targeted promotions to the segments that respond, without discounting everyone.
First visit to hundredth
How a stranger becomes a regular
1st visit
A stranger, helped anyway
No history yet. Context and product attributes carry the first session, so a brand-new visitor still sees something worth clicking.
5th visit
A picture starts forming
Views, saves, and purchases accrue, and the shelf begins reordering itself around what this person actually reaches for.
20th visit
Known by preference
The model knows what they want and what people like them wanted next, so discovery lands as often as the obvious pick.
100th visit
A regular
The store opens on what they came for. Relevance keeps climbing because every interaction is still feeding back in.
The loop
A loop that gets sharper with use
Collect signals
Views, clicks, purchases, and context flow into one profile, privacy-respecting by design.
Model preference
We learn what each person, and people like them, tend to want next.
Serve in real time
Recommendations render inline, fast, wherever they belong in your product.
Learn & improve
Every interaction feeds back, so relevance climbs the more it's used.
back to 01Behind the counter
What's actually doing the remembering
Signals, collected first-party
Views, clicks, purchases, and context flow into one profile built from behavior in your product. Nobody gets tracked across the web.
- First-party signals only
- Privacy-respecting by design
- You control what's collected
Preference, modeled
The model learns what each person tends to want next, and what people like them wanted, so cold-start visitors and regulars both get a real answer.
- Per-person preference
- Look-alike signal
- Cold-start from context
Served, then proven
Recommendations render inline and fast wherever they belong, and an A/B test against a control proves the lift on the metric you actually care about.
- Real-time, rendered native
- A/B tested against control
- Tuned toward your goal
What regulars notice
What actually changes on the floor
It's relevant on the first visit
Context and product attributes carry a brand-new visitor, so nobody waits months to be understood.
The thing they wanted isn't buried
What they came for stops being three pages deep. It's the first thing on the shelf.
It lifts the number you care about
Tuned toward conversion, order value, or retention, never clicks for their own sake.
Discovery, beyond more of the same
The mix is tuned so people also find what they wouldn't have gone looking for.
Every channel feels considered
Product picks and timing are personalized in email and lifecycle, instead of one blast for everyone.
Useful without being invasive
Relevance comes from behavior in your product, so it reads as attentive instead of creepy.
Selected work
Storefronts that rearranged themselves
What you get
An engine that earns its lift
Built into your product, tuned to your goal, and A/B tested so the impact is measured before anyone celebrates it.
- A recommendation engine tuned to your goal
- Real-time personalization served inside your product
- A cold-start strategy so new users still see relevance
- Privacy-respecting signal collection you control
- A/B testing to prove lift against a control
- Dashboards on relevance and business impact
Industry expertise
Anywhere choice can overwhelm
E-commerce & retail
Product recommendations across home, PDP, cart, and email that lift add-to-cart and order value.
Media & content
Feeds and 'up next' that keep the right readers and viewers engaged for longer.
Marketplaces
Match buyers to the listings and sellers they're most likely to act on.
SaaS & apps
In-product next-best-action that guides each user to the feature or plan that fits.
Curious what personalization would lift?
Tell us your goal, and we'll show you what an A/B test could prove
Why choose us
Built by people who prove lift with a real control
Relevant before the history exists
Context and product attributes carry the first session, so a cold-start visitor sees something useful on day one.
Tuned to your goal, never to clicks
Conversion, order value, or retention gets the tuning. Engagement for its own sake doesn't count as a win.
Proven against a control
An A/B test shows the actual lift, so the engine has to earn its place instead of being assumed to work.
First-party signals only
Relevance comes from behavior inside your product, never from following people around the web.
Discovery is part of the mix
We tune the balance so people see things they'd love and things they'd never have found on their own.
Rendered native, never bolted on
Recommendations serve inline and fast wherever they belong, so they feel like part of the product.
Why work with Flaidex
A partner that integrates instead of replacing
We integrate with what you already run
Your storefront, app, and data connect via API. Nothing gets replaced to make this work.
We handle cold-start honestly
New visitors get context-driven relevance instead of a generic "most popular" fallback dressed up as personalization.
We fit your experimentation setup
A/B testing plugs into the analytics and experimentation tools your team already uses.
We keep signal collection in your control
You decide what's collected and how it's used. Privacy is designed in from the start.
We report business impact, never vanity metrics
Dashboards track the goal you set, instead of impressions that never moved revenue.
We keep tuning as behavior shifts
The model keeps learning after launch, so relevance doesn't quietly decay as trends change.
Questions
What people ask about personalization
Does it need a lot of data to work?
It gets better with data, but it doesn't start from zero. We use context (what someone's looking at now, what similar users did, and product attributes) so recommendations are relevant even for brand-new visitors, then sharpen as behavior accrues.
Is this creepy or privacy-invasive?
It doesn't have to be. We design around first-party signals you already collect, keep personal data controlled and scoped, and can run without tracking people across the web. Relevance comes from behavior in your product, never surveillance.
How do we know it's actually helping?
We A/B test recommendations against a control and measure the metric you care about: conversion, order value, or retention. You see the lift, and we tune toward it instead of toward clicks for their own sake.
Where do recommendations show up?
Wherever they earn their place: home, product pages, cart, search, email, and in-app. We render them inline and fast so they feel native to the product.
Will it just show more of the same?
Good recommendations balance relevance with discovery. We tune for the mix that lifts your goal, so people see things they'll love and things they wouldn't have found on their own.
Can it work with our current stack?
Yes. We integrate with your storefront, app, and data, serve via API, and fit into your existing analytics and experimentation setup instead of replacing it.
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