Questions, answered.

WhyUser is the pre-launch stress test for B2B campaigns. Engineering has a staging environment to catch bugs before production, and this is the same idea for campaigns. You run a landing page, ad, or email past a simulated buying committee before the budget goes out, and you get back the role that kills it, the proof that is missing, and the fix. Four engines run in production today: Committee Simulation, Ad Campaign Simulation, Email Campaign Simulation, and Audience Discovery.

B2B deals are decided by groups, not individuals. The champion approves, the CFO vetoes, the technical lead raises a doubt that makes the budget holder walk away. Committee simulation models that chain reaction. WhyUser runs a separate agent for every seat on the committee, dozens per seat, spread across a designed range of moods and intent stages, all reading the same asset. The output is a Conflict Graph showing which role killed it and why.

A URL is enough to begin. The Ground Reality step reads your site, the public voice of your buyers in reviews and community threads, and your campaign context, then compiles your committee from that evidence. No prompt writing, no weeks of data mapping. A run takes about 30 minutes, against the review rounds that usually take weeks.

No, and we will never imply otherwise. The agents are simulated. What is real is the evidence they are built from: your buyers’ own words in reviews, community threads, and your call transcripts, with every finding traceable back to its source. So the honest description of a WhyUser output is a grounded hypothesis, not a survey result. If that distinction matters to you, grade us on it. Run a campaign whose ending you already know and see whether the simulation catches what actually happened.

A panel’s unit is the individual respondent. It shows your page to forty people separately and returns forty opinions. That is genuinely useful, and those are real humans, which ours are not. What a panel cannot do at any price is put the roles in the same room. It never sees the CFO’s veto land on the champion’s enthusiasm, because those two were surveyed independently and never interacted. That chain reaction is what WhyUser models, and the gap is structural, not a matter of panel size. Use a panel to hear real voices. Use WhyUser to watch the committee decide.

Claude writes. WhyUser measures. Keep the assistant for drafting, where it beats us outright. For a spend decision, the problem is that every run with it is a first run: ask the same page twice in two fresh sessions and you get two different answers, so you cannot tell a real fix from model drift. It is also the most patient reader alive, and none of your buyers are. It reads to the bottom while roughly 82% of cold traffic never scrolls. And single-persona roleplay is the easy half; veto propagation across a committee is the hard half. Full treatment on Claude vs WhyUser.

Here is the honest boundary. WhyUser predicts direction and rank order: which role disengages, where on the page, and which of two variants wins. It does not predict your conversion rate to a number, and any vendor promising that is guessing at variables nobody controls. Every finding ships as a hypothesis with its evidence attached, and every finding is gradeable. So the way to settle accuracy is not our claim, it is your test. Run it on a campaign you already know the ending to.

Worth knowing before you buy. It does not predict absolute conversion or bounce rates. It does not replace a live A/B test; it tells you which variant deserves the traffic. It does not audit SEO or accessibility, which need dedicated tooling. It cannot see your paid targeting settings, so it diagnoses the message, not the audience it was served to. And it will not reach content behind a login or an interaction it cannot perform, which is usually content your buyer would also fail to find.

Two mechanisms. The Evidence Tracker seals every finding as a claim you grade HIT or MISS against what actually happened, so you can see the hit rate and whether the confident calls beat the tentative ones. Lineage links each re-run to the run you fixed: change one element, re-run, and only that element re-simulates while the rest carries forward, so you see what Resolved, Persisted, or Regressed. The model holds weighted state about your committee, so run 10 is sharper than run 1.

Every finding is framed as a hypothesis and attributed to a buyer role, never to a person. The output reads “the simulation suggests the economic buyer disengaged at the ROI section because…” rather than “your page is wrong.” That is what makes the report forwardable. It moves the room from whose opinion wins to what the evidence says, which is usually the thing that unsticks a cross-functional review.

Probably. The orchestration is a weekend. The gap is everything after it. A seal store, so an untouched finding replays instead of being recomputed into a slightly different one. A verifier that proves each quote exists in the document it cites. Author classification, so a rival’s comparison page never gets read as a buyer’s words. Meaning-level identity, so this week’s finding can be matched to last month’s. None of that is exotic and none of it shows up in a demo, which is exactly why it gets skipped. And the part that compounds is not the architecture, it is the per-customer calibration: after roughly thirty graded runs the model holds about 150 fingerprints tuned to your committee, each with provenance. You can copy a sprint. You cannot copy a quarter of graded outcomes.

Built for demand gen, marketing ops, and revenue ops leaders at technical B2B SaaS companies who own paid distribution and carry a pipeline number. Not a fit for B2C or e-commerce, where the committee is shallow and the evidence carries less weight. Not a fit for CMOs at large companies, who need the artifact rather than the tool, so we work with their VP of Demand Gen instead. And not a fit for teams who believe more AI throughput alone will fix conversion, because it will not.

To start, a public URL. Simulating your own live page needs nothing from your systems, which is why we can usually show you something real on the first call. Deeper calibration is optional and comes later: call transcripts sharpen the committee, and analytics access lets us grade past findings against what actually happened. Both are opt-in, both come after you are a customer, and the product is built to be fully useful without either. The details are on our security page.

There is no charge during the access program. What we ask for instead is a 30 minute call each week to help steer product direction. After the program, WhyUser runs on an annual contract with credit-based usage: each plan includes a monthly credit allowance across the four engines, and cost scales with how much you simulate. The ROI Calculator models that against the budget a single caught campaign saves.