What is WhyUser, in one line?

WhyUser is the pre-launch stress test for B2B campaigns. Engineering has staging environments to catch bugs before production, and this is the same idea for campaigns — every finding is sourced to real buyer reviews. WhyUser is the same thing for your campaigns — stress test landing pages, ads, and emails against your End-Customer Buying Committee before the budget goes out. 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 — 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…" — not "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 | Stress Test Your B2B Campaigns Before You Spend Budget

What WhyUser is

WhyUser is the pre-launch stress test for B2B campaigns. Hundreds of adversarial buyer agents read your page, ad, or email before launch. They surface the silent vetoes, the missing proof, and the bounce risk that internal reviews and AI tools miss. Every finding sourced to real buyers. Evidence, not opinion.

Problem · Solution

Stress test every campaign before it runs.

Engineering has staging. Finance has audits. Sales runs role plays. B2B marketing still tests in production.

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

One new step, right before launch.

You don't change how you work. WhyUser adds one step between review and launch, 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 · Stress 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 stress test step to learn what is broken before launch, not 6 weeks after the budget burned.

Ground Reality

Your personas are compiled, not invented.

We don’t imagine your buyers. We compile them from your own data: your site, customer calls, community chatter, even how AI assistants describe your category. Each persona updates as new evidence arrives, so it never drifts.

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

How WhyUser compares

WhyUser vs. ChatGPT, A/B testing, internal review, and building it yourself.

An honest look, each one is best at a different job.

	ChatGPT	A/B Test	Internal Review	Agents / Claude Code	WhyUser
Works before you spend budget
Tests behavior, not just reads copy
Models the buying committee
Repeatable, same input, same verdict
Proves it on live traffic
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 probablistic models yourself and maintain it through every model update.

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

AI Opinions vs. WhyUser Simulations

Interactive · no login · nothing to install

Can't I build this myself?

The orchestration is a weekend. The trust is not.

Every piece is buildable with Claude Code. What is not a weekend is everything that makes it trustworthy. A verdict you can take to your CFO, not a chatbot's guess.

01 · The committee

A committee, not a cast.

Each role is its own agent. Miss the ROI proof and the CFO vetoes. The veto is computed, not scripted.

02 · The states

Many moods, not one.

Every role runs rushed, skeptical, and ideal, across dozens of agents. You get a pattern, not one take. Loop a chatbot 30 times and you get one opinion 30 ways.

03 · Determinism + lineage

Same page in, same verdict out.

Deterministic at the element level. Fix one part, re-run, and you know if it worked. A chatbot writes a new essay every time.

04 · It compounds

It learns your committee.

Every finding graded HIT or MISS against what happened. One loud quote stays an outlier until others confirm it. Run 10 beats run 1.

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

Who this is not for

If any of these are you, save your time.

We tell you upfront where the math doesn't work. Better than wasting your CFO's procurement cycle.

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 stress 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

A 30-minute feedback call each week with our founder. 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.