Testing in Production Testing in the Lab

Simulate Your Launch
Before You Spend.

Test your ads & pages against your virtual buyers to catch revenue leaks before they cost you real budget.

Causal Behavior AI
Multi-Sourced Ground Truth
GTM Staging Environment
Simulation_Live: deal_physics_force_map
14 Vetoes Found
Stalled Friction
Causal
Analysis
CFO (Veto)
Skeptical
Tech Lead
Convinced
Champion
Unarmed
Live Conflict Graph
Alignment Score Low (28%)
Causal Chain Events
> Tech_Lead validated integration.
> STALLED: Cannot justify to CFO.
> FRICTION: Champion lacks ROI ammo.
> Stakeholders diverging...
> VETO TRIGGERED: TCO mismatch.

Mapping deal physics for 1,200+ buying committees

Ingesting Ground Truth From Your Stack

Salesforce
Gong
Figma
LinkedIn
HubSpot
> The Problem

The High Cost of Learning by Launching

When you test with live traffic, every mistake costs you real budget.

Inefficient

The Ad Tax

Don't burn cash just to find failures that were predictable.

Cost/Lead: $420
Budget Burn: 92%
Signal Lost

The Dark Funnel Veto

Analytics miss the offline CFO who kills deals silently.

> Trace: UNKNOWN
> Attribution: None Found
> Cause: Offline Veto
Too Slow
6 WKS

The Velocity Cap

Live testing forces Marketing to move 10x slower.

Live A/B Testing: 45 Days
WhyUser Simulated: 30 Mins

Why Current Methods Fail

You are forced to choose between Fast & Biased vs. Accurate & Expensive

Internal Reviews
Echo Chamber Your team knows the product too well. They can't simulate a cold buyer with limited context or technical fatigue.
Adversarial agents that give you 10 seconds of attention, not 10 minutes of patience.
A/B Testing
Pay to Fail You buy traffic to learn what doesn't work. Costs $10k+ and takes 6 weeks to hit significance.
500 parallel simulations in 30 minutes. Find failures for $0 in ad spend.
User Panels
Say vs. Do Slow & expensive. Also people say what they think you want to hear. They don't act under real decision pressure.
See results in minutes. Behavioral simulation of decision pressure and committee dynamics.
Win/Loss Analysis
The Autopsy You learn why you lost 3 months after the deal died. Revenue is already gone.
Automated pre-flight check. Identify and resolve frictions before you ever hit launch.
> The Solution

Staging Environment for Your GTM

Simulate your entire campaign funnel in minutes to catch and fix frictions before you spend a single dollar.

1. Build

Figma / Assets

2. Review

Internal Alignment
NEW

3. Simulate

Pre-Flight Risk Check

4. Deploy

Confident Launch

How It Works

From Draft to Decision in 30 Minutes

STEP 01

Ingest Your Reality

Reverse-engineer your buyer from Gong transcripts and CRM footprints.

> Parsing Gong & CRM...
> Synthesizing Twins...
> Ground Truth READY
STEP 02
Champion
CFO

Simulate

Model adversarial committee conflict to find exactly where deals die.

> Adversarial Mode...
> CONFLICT DETECTED
> CFO blocked deal.
STEP 03
PLAYBOOK
Add ROI Calc
DE-RISKED

Fix & Launch

Generate a dev-ready playbook to resolve frictions before market launch.

> Generating Proof...
> Friction Resolved.
> READY FOR MARKET
> Capabilities

Validate Your Funnel at Every Stage

Red Team Assembly & Ranking

Scans job descriptions to build a buyer Red Team. Simulates engagement to rank the top 5 converting titles for your campaign.

Scanned: 12 Verticals Candidates: 142 Shortlist: 5
Time: 1.2s
#1
DevOps Manager 98% Resonance
Vertical: FinTech Alt: Platform Lead
Why Chosen: Content mentions "Automated Compliance" and "Terraform." Matches 9/10 keywords in this role's specific JD.
#2
Site Reliability Engineer (SRE) 85% Resonance
Logic: Resonates with "Uptime" focus, but high friction detected on "Sales-Led" pricing model for this title.
Shortlist size: 5 Roles
> Ground Reality

Constructing Your Ground Reality

We don't imagine your buyer. We reverse-engineer them from your actual data footprint.

1. Vendor DNA

Source: Your Site & Docs

Crawls your public docs and technical guides to map structural constraints like "Sales-Led" or "Self-Hosted Only".

2. Market Intel

Source: G2, Reddit, Peers

Scrapes community sentiment and competitor gaps to ground agents in real-world B2B buying pressure.

3. Customer Voice

Source: Gong / CRM Transcripts

Ingests redacted transcripts to extract the exact technical and financial rejection reasons your team hears daily.

4. Context

Source: Your Hypotheses

Injects your institutional knowledge. Your specific ad promise becomes the agent's primary active search goal.

Technical Differentiation

Why Can't I Just Use an LLM?

General purpose models are built for writing, not for adversarial behavioral analysis.

Generic AI
mode: creative_writing
Reads, doesn't hunt

It confirms "Yes, the ROI calculator exists." It doesn't scroll, search, or get frustrated when it's buried under 3 layers of navigation.

Personas in a vacuum

"Act like a CFO" produces plausible text. It doesn't model the friction between a Tech Lead's excitement and a CFO's financial skepticism.

Generic training data

It knows what a "generic CFO" says. It doesn't know what the CFO who rejected your last 5 deals says or why they were skeptical.

W
WhyUser
mode: causal_simulation
Simulates behavior

Agents scroll, scan navigation, and click. If your proof is buried, they fail to find it and bounce. We test findability, not just presence.

Models committee conflict

We simulate how technical doubt triggers financial vetoes. We find exactly where deals die between different stakeholders.

Your customer data

Agents are trained on your Gong transcripts and CRM rejection codes. They know your market reality, not just the generic internet.

> Get Started

Fail in the Simulator.
Win in the Market.

Don't wait for the post-mortem. Identify and fix your revenue leaks before you spend $50k on a broken campaign.