AI-native experiment design — Open source

From hypothesis
to outcome.
Faster, smarter,
with evidence.

The AI platform for product and growth teams who treat experimentation as a competitive advantage — not an afterthought. Statistical rigor, financial modeling, and stakeholder-ready decisions in minutes, not months.

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Trial Length Optimization — 3-day vs 7-day vs 14-day · 90% confidence · ~98k registrants · 94 days to readout Pricing Page Experiment — Monthly vs Annual anchor · MDE 8% relative · $0 additional spend Onboarding Flow — 4-step vs 7-step · Activation guardrail · 80% power · 60-day retention gate Paywall Placement — Session 1 vs Session 3 · LTV primary metric · CAC payback modeled Trial Length Optimization — 3-day vs 7-day vs 14-day · 90% confidence · ~98k registrants · 94 days to readout Pricing Page Experiment — Monthly vs Annual anchor · MDE 8% relative · $0 additional spend Onboarding Flow — 4-step vs 7-step · Activation guardrail · 80% power · 60-day retention gate Paywall Placement — Session 1 vs Session 3 · LTV primary metric · CAC payback modeled
Our Positioning

experimentation.ai is the AI-native platform for product and growth teams who treat experimentation as a competitive advantage — not an afterthought.

⚡
Speed Without SacrificeFrom question to statistically-powered experiment in minutes. No PhD required.
🎯
Strategic, Not Just StatisticalFull-funnel financial modeling: LTV, CAC payback, cash velocity, retention guardrails.
🛡️
Bias Toward ActionPre-defined decision rules. Shorter trials that don't harm LTV are a business win, full stop.
The Problem

Most teams run
experiment theatre.

Product and growth teams know they should experiment. The reality is underpowered tests, misread results, ignored guardrails, and decisions made months after the business context has shifted. You're not getting insights — you're getting confirmation bias with extra steps.

01
Wrong Intuitions
Features ship based on gut feel, then rationalized post-hoc. There's no real feedback loop — just confirmation bias dressed up as process.
02
Guardrails Ignored
Teams optimize trial conversion while inadvertently destroying 90-day retention. Financial implications aren't modeled until it's already too late.
03
Analysis Paralysis
Statistical rigor requires expertise most teams don't have in-house. Data scientists are expensive, scarce, and perpetually context-switching.
04
Slow Time-to-Insight
By the time a proper experiment concludes, the business context has shifted. Speed and thoroughness have always felt like a trade-off. They shouldn't be.
05
Capital Misallocated
Without evidence-based capital allocation, marketing spend chases the wrong levers. Monetization efficiency suffers. CAC payback extends indefinitely.
How It Works

AI does the methodology.
You bring the business context.

01
💡
Define the Question
Start with the strategic question: Is urgency a monetization lever? Does shorter onboarding improve activation? Business context comes first, always.
Business Context
02
🤖
AI Designs the Experiment
Statistically-sound hypotheses, the right metrics framework, sample size calculations, traffic allocation rationale, and guardrail flags — before you launch.
Statistical Rigor
03
📡
Run & Monitor Live
Dashboard tracks primary metrics, leading indicators, and guardrail signals in real time. Directional risks surface before they become post-launch regrets.
Live Intelligence
04
✅
Confident Decision
A stakeholder-ready brief with rollout plan, financial impact model, retention gate, and risk assessment. From question to confident answer — in days.
Actionable Output
Why It Works

Built on the principle that rigor should be fast.

⚡
Speed Without Sacrifice
From strategic question to fully-designed, statistically-powered experiment in minutes. No PhD required. AI handles the methodology; you bring the business context.
🎯
Strategic, Not Just Statistical
We model the full business impact: LTV per registrant, cash velocity, CAC payback, retention guardrails. Every decision is financially grounded from day one.
📋
Automated Experiment Briefs
Professional, stakeholder-ready briefs with pre-registered hypotheses, metrics frameworks, decision trees, and rollout plans. The brief you'd write after 10 years of practice — in 10 minutes.
🛡
Built-In Guardrails
Automatically surface underpowered guardrail metrics, directional-only reads, and retention risks before they become post-launch regrets.
🗂️
Bias Toward Action
Pre-defined decision rules before the test runs — no post-hoc rationalization. Improved cash velocity is a standalone business win. We're built around that framework.
🔄
Compounding Intelligence
Every experiment makes the next one smarter. The platform learns your funnel metrics, baseline rates, and risk thresholds — building institutional knowledge that doesn't walk out the door.
10×
Faster experiment
brief generation
94%
Reduction in statistical
design errors
3 days
Average from question
to live experiment
$zero
Additional marketing
spend required
Who It's For

Built for every team that asks "what should we test next?"

Caspar Lee
Caspar Lee
VP of Product, Series B SaaS
VP of Product &
Head of Growth

"I need experiment results that hold up in a board meeting and don't require a data scientist to interpret."

  • Design experiments that pass statistical scrutiny without the back-and-forth
  • Stakeholder-ready briefs in minutes, not days
  • Financially-modeled rollout decisions tied to LTV and CAC
Nevean Nawar
Nevean Nawar
Senior PM, Growth
Senior PM &
Growth Manager

"I want to run rigorous experiments without waiting weeks for data science bandwidth."

  • Correct sample sizes and traffic allocation, every time
  • Pre-registered hypotheses and pre-defined success criteria
  • Live guardrail monitoring and early directional signals
Abe Stanway
Abe Stanway
CFO, Growth-Stage Tech
CFO, CEO &
Board Member

"Capital efficiency starts with knowing what actually drives LTV — not just what drives clicks."

  • Evidence-based capital allocation strategy, not gut-feel
  • CAC payback period and cash velocity modeling built in
  • Risk-gated rollout decisions with 90-day retention gates
Competitive Position

No one else owns AI-native, strategy-first experimentation.

A/B Testing Tools — Optimizely, VWO
Execution-only. Strong at running tests, silent on design quality or business implications.
We design the experiment and model the financial outcome — not just run it.
Analytics Platforms — Mixpanel, Amplitude
Measurement and dashboards. They tell you what happened, not what to test or how to test it.
We answer "what should we test next and how?" — not just "what did we see?"
In-House Data Science Teams
Expert, expensive, slow. A luxury reserved for Series C+ companies with deep pockets.
We give every team access to Series C-grade experimentation rigor, instantly.
General AI Assistants — ChatGPT, Claude
Helpful but generic. No experimentation-specific methodology or financial modeling layer.
Verticalized, opinionated, and calibrated specifically for experiment design and monetization strategy.
Open Source

Stop guessing.
Start testing.

Open source. Clone it, run it, make it yours.

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