MarketrixHow Marketrix works
Point Marketrix at a live website or app. It builds Knowledge, drafts Personas grounded in 3M+ real profiles, and dispatches them through a real browser — so you get a structured read on how twelve kinds of buyer weigh the same decision, with the reasoning attached, in hours instead of weeks.
What we read about your product: crawled pages, PDFs, videos, analytics exports, support tickets.
Layered simulated users grounded in Persona OS and your own evidence. Every quote is attributed to one.
Agents drive a real browser through your live product, step by step, with screenshots and reactions.
Muscle memory distilled from successful runs, versioned and reused so later runs are faster and cheaper.
What we see: the Application Graph built from simulations — pages, sections, edges and heatmaps.
UX Research, Surveys and A/B Studies at panel scale — thousands of participants in an afternoon.
Real humans answer on the meet platform; transcripts fold straight into a persona's grounding layer.
Author-built questionnaires for real respondents. Reports filter Organic / Simulated / Both without merging provenance.
An embedded widget that answers in context, validated by personas who used the product first.
The base layer comes from Persona OS — real profiles, real job titles — and the grounding layer comes from your transcripts and knowledge. A persona is not told what to value; it is given priorities and left to discover whether your product meets them.
12 persona types become 10,284 participants because each type runs hundreds of independent persona-users. When a type leans one way you can see whether it leans consistently or is split.
A finding's strength is the count of distinct personas among its quotes, never a self-graded number. Every quote is attributed by persona id and run, and provenance is stamped by the platform, not the model.
A simulated respondent emits only what a person could answer: a choice, a stop step, free text, an ordinal certainty. Rates, bounds and gates are computed on read from counts, so a what-if can never drift from the headline.
A Driver calls the shots and emits an action per step. A Rider observes and reacts. The gates are orthogonal, so a generic Driver produces an action transcript with no reaction, and a Rider persona produces reactions with no actions.
The same AI-heavy spender that drove the A/B study also answered the survey, reacted in the onboarding UXR and drove the pricing QA flow — so findings connect across tools.
- 1Personas — Open the AI-heavy spender. Show the three layers, the evidence tab, and chat with it in character.
- 2A/B Study — Mercury vs. Bank of America — the 10,284-participant headline, the per-persona split, and why Bank of America did not lose everywhere.
- 3UX friction — Onboarding friction — the SSN/ITIN drop-off at step 4, the friction list with fixes, and step-by-step reactions.
- 4Survey — Business banking priorities — a real Google Form completed by 480 simulated respondents, with consensus and skip funnel.
- 5QA Flow — Pricing & plan comparison — 32 outcomes across roles and viewports, a real mobile failure, and indecisive outcomes you can rule on.
- 6Workflows — GitHub PR → QA Check, the nightly regression, and the Slack /simulate command.