Monty-UX learns who your users are from how they respond to change: tiny, safe shifts in your interface that sort your audience by behavior, in real time. Then it tells you what any change will mean to every valuable user group, before you ship it. All inside guardrails you define.
Figuring out the best version of even an everyday page is real work: research rounds, honest testing, a data team most companies can't spare. So most teams don't. They ship for a few target users and call it done, and everyone outside that target quietly gets an experience built for someone else.
Even among the people who already love your product, the winning experience splits. One group wants the image up top and one tap to buy. Another wants the specs and a way to compare. A third trusts the reviews before anything else. Same content, three different ideas of “best.”
One product, three cluster-tailored layouts. Every group has a version that wins for them; a single design can only pick one.
In a few clicks, someone reveals more about the experience that actually fits them than you could buy from any data marketplace. Monty-UX just gets them to show it: it serves small, safe variations whose only job is to divide the crowd, and each reaction sorts a person into a sharper group than the one before. Track only what earns a genuine response, keep splitting, and the segments get finer on their own. Your interface runs its own experiments, on a loop.
Do that continuously and something falls out for free: a live map of who your users actually are and what each of them wants. Optimization stops being a guess and becomes a lookup. It is designed to:
Monty-UX serves small, safe variations with one job: split your audience by how they respond. One lightweight async script tag instruments your product, and a founder-guided onboarding compiles it into a typed grammar of entities, actions, and your guardrails, so every variation is checked before a 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, behavioral clusters can be cross-referenced against your own CRM segments, and Monty-UX never receives your demographic data.
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 response splits the crowd finer, and sharpens the next variation
Every variation is both a question and a signal, served on an explore/exploit balance you set, at a learning rate you control.
Every week, Monty-UX surfaces the experiments most worth running, each scored against every behavioral cluster it has found. These are not your marketing personas. “New visitor” and “power user” are stories you tell yourself; the real groups are emergent, drawn from how people actually move through your product. A retiree in Mobile and a teenager in Connecticut can land in the same group. We call them Group A, B, C, because honestly, that is what they are.
Red is the share of a group 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.
Sample report: illustrative data, shown at the fidelity the weekly report delivers: segment sizes, intervals, and the occasional honest “keep testing.”
…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.
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, cross-references against your own CRM segments), 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-traffic partners: at hundreds of millions of monthly sessions, the volume lets us reach statistical significance on even highly granular clusters and hypotheses in days, not quarters.
Monty-UX doesn't redesign your product. It hands the people who do the work, 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, designers get a specific hypothesis and the evidence behind it, the same starting point as a strong brief, without months of discovery to get there. What to do with the signal stays a design decision.
Every PM 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.
Build against evidence, not opinions. Small changes that provably move numbers set a floor, and every roadmap item ships with a number to beat, and nothing lands in a sprint without a reason it will matter.
The culture shifts from putting out fires to pre-emptively discovering what your users will care about next, and the road ends at per-segment serving: every user group getting the version of your product that fits them best.
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, with residual risk bounded by the guardrails you declare and the checker's coverage.
Integration is deliberately boring: one async script tag (CSP-compatible, tag-manager friendly, with a no-script fallback), plus two services beside your existing stack. An experiment lookup your front end reads at request time, and a batch analysis service that never touches the serving path. No re-platforming, no SDK sprawl, nothing between your users and your uptime.
The framework (the grammar, the safety guarantees, and the segmentation mathematics) is specified properly in our whitepaper.
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?