Questions, answered.

WhyUser is the pre-launch stress test for B2B campaigns. Engineering has a staging environment to catch bugs before production; this is the same idea for campaigns, with every finding sourced to real buyer reviews. You stress test landing pages, ads, and emails against your buying committee before the budget goes out. 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 hundreds of adversarial agents per role, each playing a specific committee seat, against the same asset. The output is a Conflict Graph showing which role killed the deal and why.

Your reality, not a long onboarding. WhyUser’s Ground Reality step ingests your site, your customer voice (reviews, calls, community), and your campaign context in minutes, and compiles your buying committee from that evidence. You are not writing prompts or mapping data for weeks; the personas are built from your real buyers and keep learning as new evidence arrives.

WhyUser produces hypotheses, not verdicts. Every finding is a statistically grounded signal worth checking against your own data. The way to calibrate it is to run it on a campaign where you already know the outcome. If the simulation catches what actually happened, the accuracy question is answered by your data, not our claims.

A chatbot gives you one plausible opinion optimized to be helpful. WhyUser runs hundreds of adversarial, matched-pair agents and finds the pattern that holds across runs. That finding is statistically defensible, you can take it to a CRO. A single chatbot opinion is not. Different tool, different job.

Two ways. The Evidence Tracker seals every finding as a claim you grade HIT or MISS against the real outcome, so you can see the hit rate and whether the high-confidence calls beat the low-confidence 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, not a verdict. The output reads “the simulation suggests the economic buyer disengaged at the ROI section because…” not “your page is wrong.” That makes the artifact safe to forward to Content and Brand without it landing as a personal critique, which is why cross-functional reviews actually move forward.

Probably, the orchestration is a weekend project. The gap is everything after. A raw agent swarm writes a new essay every run, so you cannot tell a real fix from model drift; WhyUser is deterministic at the element level, so re-runs are comparable. Looping one model 30 times gives you one opinion 30 ways; WhyUser runs each committee role as its own agent across set states, so the spread means something. And the moat is not the orchestration, it is the per-customer model: after about 30 graded runs it holds roughly 150 fingerprints tuned to your committee, each with provenance. You can copy the architecture in a sprint, not the calibration earned over a quarter. Build it and you own simulation infrastructure you re-test through every model update, with one customer: you.

WhyUser is built for demand gen, marketing ops, and revenue ops leaders at technical B2B SaaS companies who own paid distribution and pipeline. It is not a fit for B2C or e-commerce, where buying committees are shallow, or for teams who believe more AI throughput alone will fix conversion.

Design partners are no-charge during the program. After that, WhyUser uses an annual contract with a credit-based usage model: each plan includes a monthly credit allotment for simulation runs across the four engines, and pricing scales with your simulation volume. The ROI Calculator models your cost against the budget a single caught campaign saves.