Your recommendations went stale
A model trained once assumes the future looks like the past. Your catalogue doesn't hold still long enough for that to stay true.
AI for e-commerce
The recommendation engine that also explains itself.
MLArt builds recommendation, ranking, search, and pricing systems that run in production — and, new this chapter, a generative shopping assistant grounded in what your model actually decided, not a guess dressed up as an answer.
Sound familiar?
Generic recommendations stop working the moment things change.
New products can't get ranked
No sales history yet means no signal to rank on — until the item has sold enough to prove itself, which is exactly when it needs exposure most.
Your catalogue is under-tagged
Search, filtering, and recommendation all read from attributes that were never filled in consistently, if at all.
How it plays out
One model, the whole relationship.
The same signal that attracts a shopper keeps working after they've bought.
- Attract
Show up in search and social with the right product, not a generic ad.
- Engage
Read live intent — search terms, browsing, cart — and respond with relevance.
- Convert
A personalised offer, at the moment it can still change the decision.
- Win-back
A high-value nudge, timed from behaviour, brings a lapsed shopper home.
Explainable ranking
Ask any recommendation why it's there.
A ranking you can't interrogate is a ranking your merchandisers can't trust or correct. Every placement MLArt makes carries the signals that produced it — weighted, inspectable, and available to your team as well as to us. Open a tile to see the shape of that breakdown.
match0.91
Signals behind this placement
- Matches recent search82%
- In stock, ships today100%
- Similar to last purchase61%
- Trending in this category44%
match0.86
Signals behind this placement
- Fits your usual size88%
- Viewed twice this week73%
- Back in stock in your size100%
- Rated well by similar shoppers57%
match0.74
Signals behind this placement
- Bought with items in your basket79%
- Seasonal demand climbing66%
- New arrival, no sales history35%
match0.69
Signals behind this placement
- Matches saved wishlist colours71%
- Completes a set you own64%
- Within your usual price band58%
- Popular in your region41%
Illustrative example — not connected to a live catalogue.
What a recommendation reads
Illustrative example
From the case study
One catalogue, one engine, measured against what it replaced.
- 30%Lift in click-through on recommendations
- 1M+Products in the live catalogue
- 5Engineering modules behind the ranking
- 100%Deployed in the client's own infrastructure
From the case study — a 2022 engagement, read in full for the method and the caveats.
What we build
Three things, one decision loop.
Predictive personalization and adaptive recommendation are the core. Generative commerce is the newer half — grounded in the same catalogue, never invented.
Predictive Personalization
The right offer, to the right shopper, at the moment they're deciding.
Learn moreAdaptive Recommendations
A policy that keeps adjusting as the catalogue, the season, and the shopper change.
Learn moreGenerative Commerce
A shopping assistant grounded in your live catalogue — the chapter that didn't exist in 2022.
Learn moreBring us your catalogue.
Tell us what you sell and where it lives. We'll tell you honestly what we'd build, and what you'd own at the end.