Monty-UX learns who your users are from how they respond to change — tiny, safe micro-shifts in your interface that segment your audience by behavior, at runtime. Then it tells you what any change will mean to every valuable user group, before you ship it. All inside guardrails you define.
Change an element in way A, and one kind of user responds in style i — another kind in style j. Every micro-shift is a question; every response is an answer. Run that across your entire audience and Monty-UX drills down to user-level personalization fast — while building something nobody else has: a live map of how much each of your user groups cares about each kind of change. It is designed to:
Declare your guardrails, add one line of JavaScript. Monty-UX serves small, safe micro-shifts in your interface — each generated inside a typed grammar and mechanically checked before any user ever sees it.
Different users respond to the same shift in different ways — so responses cluster your audience at runtime. No cross-site cookies, no bought data. For logged-in users, clusters map straight onto your target demos and revenue buckets.
Every change makes some users happy and some sad. Each cluster gets an elasticity profile per kind of change — who's delighted, who's hurt, and by how much — so you know exactly which users a change touches.
Monty-UX finds the combinations of changes that thrill your target groups while minimizing damage to everyone else — with error bars on expected churn and DAU lift, per valuable-user group, before you ship.
Every experiment is a widget; every widget is both a probe and a signal — served on an explore/exploit balance you set, at a learning rate you control.
No product decision should ride on a strong hunch. Monty-UX finds the cracks in the seams of your experience — and identifies exactly who would respond if you put an entire team against one.
Today, you ship a redesign and watch the big numbers, hoping. With Monty-UX, you ask first. Because every user cluster carries an elasticity profile — and, for your logged-in users, maps onto real demos and revenue buckets — we can tell you what moving elements a, b, and c will do to each group that matters: expected churn, expected DAU lift, with error bars, per valuable-user group. What a change means to the sum of all your users, at once, in detail — before the change.
This is why we build with high-throughput partners: working with Yahoo properties like Engadget, the traffic volume lets us reach statistical significance on even highly granular clusters and hypotheses in days — not quarters.
Every week, Monty-UX surfaces the interface experiments most worth running — each one scored against every user segment before anyone commits. Red is the share of a segment a change pushes the wrong way; green is the share it moves forward. The median line is where your product stands today.
Surface the review summary beside the product title instead of below the specifications block.
Replace the left filter rail with a single natural-language search-and-filter bar across the top.
…and eight more in this week's report. Because every flight is scored per segment, you don't ship to everyone — each segment gets the version that wins for them.
Monty-UX doesn't redesign your product. It hands the people who do — your designers, PMs, and engineers — the evidence they've never had: what to build, for whom, and what it's worth, before a single sprint is committed.
When micro-shifts reveal a segment straining against a flow, your design team gets a rebuild brief — not a template tweak: a thesis for the user group a true redesign will win, and the evidence that the risk to every other key group is low. Real craft, aimed by data.
Every PM, engineer, and designer effectively gains a data-science team running incrementality experiments — before any company resources go to a POC or a feature flight. Weak ideas die for the cost of a micro-shift; strong ones arrive with proof attached.
Small changes that provably move numbers set a floor — every roadmap item now ships with a number to beat. That pressure changes the culture: from putting out fires to pre-emptively discovering what your users will care about next.
No-touch analytics, automated experimentation, and optimal-decision proofs inside the option space you define. Per-segment serving — every user group getting its winning version — is the road we're building with our design partners.
Every experiment is generated inside a typed grammar of your product — your entities, your valid actions, your brand rules — and verified by a deterministic checker before it is ever served. Proposals that fail validation are never seen by a user. Because generation happens offline and expands to production code deterministically, each additional experiment carries near-zero marginal cost — and zero marginal risk.
The framework — the grammar, the safety guarantees, and the segmentation mathematics — is specified properly in our whitepaper. Read it →
We're in private preview with a small number of high-engagement sites while we test and refine our models. Want your users to meet their best version of your product?