What is WhyUser, in one line?

WhyUser is the pre-launch test for B2B campaigns. Engineering has staging environments to catch bugs before production, ops runs dry runs, and marketing goes straight to opening night. WhyUser is the rehearsal: it tests landing pages, ads, and emails against your End-Customer Buying Committee before the budget goes out, and every finding is sourced to real buyer reviews. Four engines in production today: Committee Simulation, Ad Campaign Simulation, Email Campaign Simulation, and Audience Discovery.

What is buying committee simulation?

B2B deals are decided by groups, not individuals. The Champion approves. The CFO vetoes. The Technical Decision Maker raises a doubt that triggers the Budget Holder to walk away. Buying committee simulation models that chain reaction. WhyUser runs hundreds of adversarial agents per persona, each playing a specific committee role, on the same asset. The output is a Conflict Graph showing which persona killed the deal and why.

How accurate are buying committee simulations?

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 by our claims.

Why not just use ChatGPT or Claude?

ChatGPT gives you one plausible opinion optimized for being helpful. WhyUser runs 300 adversarial matched-pair agents and finds the pattern that emerges across runs. The finding is statistically defensible, so you can take it to a CRO. A single chatbot opinion is not. Different tool, different job.

Can I just build this myself with Claude Code and agents?

Probably. The orchestration is a weekend project. The gap is what comes 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 a separate agent across set states, so the spread means something. And the moat is not the orchestration, it is the per-customer model. After 30 graded runs it holds about 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, re-tested through every model update, with one customer: you.

How does WhyUser get smarter over time?

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 unchanged, so you see what Resolved, Persisted, or Regressed. The model holds weighted state about your committee, so run 10 is smarter than run 1. Personas are compiled from your real buyer data, not invented, and update only as new evidence arrives, so they do not drift.

Will Brand and Content see this as a critique?

We frame everything as hypothesis, not verdict. The output language is "the simulation suggests the Economic Buyer disengaged at the ROI section because…" rather than "your page has X." This makes the artifact safe to forward to Content and Brand without it being a personal critique. Customers tell us this is why their cross-functional reviews actually move forward.

Design partners are no-charge during the program. 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. Pricing depends on your simulation volume. Visit the ROI Calculator to model your cost and ROI.

WhyUser | See How Real Buyers Read Your Page Before You Pay For Traffic

What WhyUser is

WhyUser is a buying committee simulation that runs before you launch a B2B campaign. Your internal reviewers already know the product, so they read the page as ideal users. Real buyers arrive skeptical and distracted. WhyUser simulates hundreds of those buyers, one agent for every seat on the buying committee, and each agent is compiled from your own sales calls, reviews, community voice, and competitor voice. It surfaces the silent vetoes, the missing proof, and the bounce risk that internal reviews and AI chatbots miss. Every finding cites the buyer evidence behind it, so what you get is evidence you can forward, not an opinion you have to defend.

Problem · Solution

Marketing is the only team that opens without a rehearsal.

Engineering has staging. Ops runs dry runs. Sales rehearses the pitch. Marketing goes straight to opening night, then buys traffic to find out whether the messaging worked. WhyUser is the rehearsal. It runs your ad, email or landing page past the four or five people who have to say yes, then returns who walked away, what proof was missing, and the fix.

Silent veto

“The champion loves the page. The CFO quietly vetoes. Nobody tells you.”

Engine 01

Committee Simulation

Which buyer role killed it, and the missing proof.

Conflict Graph

Wasted spend

“Ad budget pours into a page that was never going to convert.”

Engine 02

Ad Campaign Simulation

The ad-to-page gap, caught before you spend.

Kill Sheet

Flat sends

“One email to three roles wins for none of them.”

Engine 03

Email Campaign Simulation

The subject and CTA that win for each role.

Per-Persona Winner

Stale targeting

“You target last year’s titles and bid against every competitor.”

Engine 04

Audience Discovery

The roles your content actually fits.

Ranked Title Pool

WhyUser gives me the ammunition I need to make the case internally. To engineering. To leadership. To sales. It's not just telling me a page is broken. It's showing me exactly which buyer role bounces, why, and what to fix. That's gold for a lean marketing team.

Veronica Dominicis · Head of Demand Gen & RevOps · Vectara

Where WhyUser fits

Not another review round. The one that ends them.

You don't change how you work, and you don't add a cycle. WhyUser replaces the back and forth between review and launch with one 30 minute answer, in the gap where silent vetoes slip through today. Run it in-app, or headless via API.

Phase · 01 Plan Brief & ICP

Phase · 02 Build Copy, design, ad creative

Phase · 03 Review Internal alignment

New step · pre-launch

WhyUser · Test

Test the ad, email, or page against the buying committee. See which persona vetoes, what proof is missing, where the bounce risk hides, sourced to real buyer reviews.

30 min Headless via API Sourced Before spend

Phase · 05 Launch Push live, allocate spend

Phase · 06 Measure Live performance, A/B

Your team already runs Plan → Build → Review → Launch → Measure. WhyUser is the 30 minute test step to learn what is broken before launch, not 6 weeks after the budget burned.

Ground Reality

Not imaginary AI characters. Not a static profile with a chatbot attached.

We don’t imagine your buyers. We compile them from your own evidence: your site, customer calls, community voice, competitor voice, and the institutional knowledge your team already carries. Each persona updates as new evidence arrives, so it never drifts. That is why a finding is forwardable instead of arguable.

Company Context

site_crawl · acv · gtm

Customer Voice

gong · crm_objections

Market Intel

community · competitor_gaps

Buyer Agent

n=300 / run

AI Awareness

perplexity · chatgpt · claude

Campaign Context

ad_promise · landing_url

A/B Outcomes

live_perf · feedback_loop

Six grounded inputs become one buyer agent, with a role, a mood, and a way of deciding. Every run fields 300 of them across 5 committee roles.

Why ngrok chose WhyUser

Most tools require weeks of manual data mapping and setup before you can even plan or launch a campaign. WhyUser's 'Ground Reality' onboarding ingested our site, social sentiment, and customer transcripts in minutes, creating a foundation that actually understands our business and users. The platform operates from that understanding instead of relying on prompting. You're not starting from scratch every time you test, and it keeps learning.

Nichole Larue · Head of Marketing Operations · ngrok

What makes a simulation trustworthy

Four things that separate a simulation from a guess.

Anyone can ask an AI what it thinks of a page. Four things turn that opinion into a verdict you can act on: one agent per committee role, many behavioral states per role, determinism, and a graded track record. That is a finding you can take to your CFO, not a chatbot's guess.

01 · The committee

A committee, not a cast.

WhyUser runs one agent per committee role, not one agent playing five parts. Miss the ROI proof and the Economic Buyer vetoes. The veto is computed from the evidence the page is missing, never scripted in advance.

02 · The states

Many moods, not one.

Every role is run in three behavioral states: rushed, skeptical and ideal, across dozens of agents each. You get a distribution, not one take. Loop a chatbot 30 times and you get one opinion 30 ways.

03 · Determinism + lineage

Same page in, same verdict out.

WhyUser is deterministic at the element level: the same page in produces the same verdict out. Fix one section, re-run, and you know whether that change is what moved the result. A chatbot writes a new essay every time.

04 · It compounds

Your 10th simulation is better tuned than your 1st.

WhyUser keeps learning about your buyers with every run. Each finding is graded HIT or MISS against what actually happened, and the result carries forward. One loud quote stays an outlier until other buyers confirm it.

EVIDENCE LEDGER Acme · /platform

82% hit rate · 41 graded calls

Econ Buyer vetoes at ROI section HIT

Hero clarity gap (you fixed in v3) RESOLVED

Security badge missing above fold MISS

                                ~150 calibrated fingerprints · each with provenance · deterministic updates
                            

Lineage and the Evidence Tracker, from one run. Each version only re-tests what you changed; the rest carries forward unchanged.

See these four ideas live: AI Opinions vs. WhyUser Simulations

How WhyUser compares

Five ways to pressure-test a campaign. We do not win every row.

Chatbots give you opinions. WhyUser gives you simulations that are reproducible and gradable. Below is an honest look at five ways to pressure-test a B2B campaign. Each one is best at a different job, and WhyUser does not win every row.

	ChatGPT	A/B Test	Internal Review	Agents / Claude Code	WhyUser
Works before you spend budget	Yes	No	Yes	Yes	Yes
Tests behavior, not just reads copy	No	Yes	No	Partial: Possible - but you build and maintain it	Yes
Models the buying committee	No	No	No	Partial: Possible - but this is the hard part you would be building	Yes
Catches "every role approves, deal still stalls"	No	No	No	No	Yes
Repeatable, same input, same verdict	No	No	No	No	Yes
Proves it on live traffic	No	Yes	No	No	No
Time to first result	instant	4–6 weeks	~2 weeks	weeks to build	~30 min
What it costs	free	$10K+ in traffic	team hours	build + upkeep	credit-based

Agents / Claude Code can build these, but you build the infrastructure, state management and determinism of probabilistic models yourself and maintain it through every model update.

Every other method reads the page. Only a committee graph scores the handoff between roles, which is where a page that everyone approves still fails to move a deal.

Full comparisons, no gate: vs. ChatGPT and Claude · vs. internal review · vs. building it yourself · vs. buyer panels

See the difference for yourself, one landing page, judged two ways.

AI Opinions vs. WhyUser Simulations

Interactive · no login · nothing to install

Who this is not for

If any of these are you, save your time.

WhyUser is a poor fit for four groups, and we would rather say so before you spend a procurement cycle on it. We tell you upfront where the math doesn't work.

CMOs at 500+ person companies.

Your team needs the artifact, not you. We sell directly to your VP of Demand Gen instead.

Product marketing or content roles.

Different sale shape. We focus on the buyer who owns paid distribution and pipeline accountability.

B2C, e-commerce, or non-technical SaaS.

Buying committees are shallow here, so the evidence carries less weight. The math doesn't work for you yet.

Marketers who believe more AI throughput will fix conversion.

It won't. Come back after the campaign that taught you that.

Get Started

Fail in the test.
Win in the market.

Tell us about the next campaign you have going live. We respond within 48 hours.

The Cost

No charge during the program. Post-program: only if it earns its place in your stack.

The Trade

One short feedback call with our founder while you are in the program. We use it to guide product direction.

The Fit

You drive B2B traffic. Campaigns going live in the next 30 days. You can change them.