A/B Testing Automation —
Scientific CRO at Scale.
AI-powered A/B testing on autopilot. Multi-armed bandits auto-allocate traffic to winners. Variants generated, deployed, measured, optimized — without your input.
Test everything. Optimize always.
Landing Page Elements
Hero headlines, CTAs, images, social proof, urgency
Email Campaigns
Subject lines, preview text, send times, content blocks
Ad Creative
Meta/Google ad variants tested and winners scaled
Pricing Pages
Price display, anchor pricing, plan arrangement
Checkout Flow
Step count, form fields, trust signals, upsells
Product Pages
Images, descriptions, reviews placement, CTAs
“Auto-testing runs 50+ tests per month. Our checkout CVR went from 2.1% to 6.3% in 6 months of compounded wins. We haven't manually set up a test since.”
Scientific CRO, automated.
Most A/B testing is undisciplined — underpowered tests, significance cheating, vanity wins. We build automated A/B platforms with stat-significance discipline + multi-armed bandits to accelerate learning.
Audit + infrastructure
Week 1-2Current testing platform audit, experiment-library review, statistical-power analysis, event-data validation, server-side testing infrastructure design.
Testing platform build
Week 3-5Server-side testing framework, experiment assignment via feature flags, event-tracking hygiene, pre-registered tests, significance-calculation automation.
Multi-armed bandit + automation
Week 6-8MAB for continuous optimization, auto-allocation to winners, Slack + email alerts, Notion-based experiment library, weekly retrospective cadence.
Test velocity + learning
Ongoing5-15 experiments / month, winning tests documented + compounded, losing tests killed fast, cross-team testing culture built.
A full CRO + experimentation system.
Infrastructure, methodology, tooling, and cultural practices — built to compound wins over years.
Testing platform
Server-side experimentation via GrowthBook / Statsig / LaunchDarkly. Feature flags for safe rollouts, experiment assignment at the edge, no client-side flicker.
Stat-significance discipline
Pre-registered hypotheses, minimum detectable effects, power calculations, sample-size locks. Bayesian + frequentist both supported. No p-hacking.
Multi-armed bandit
For optimization where learning velocity matters more than final effect size. Auto-allocates traffic to winning variants with statistical discipline.
Experiment library + retros
Every test documented in Notion — hypothesis, design, results, learnings. Compound value over years via searchable decision history.
Built for high-traffic web + product teams.
A/B testing pays off with volume. Need ≥10K sessions / week on tested pages for statistical power. Enterprise products with 100K+ daily actives unlock massive compound wins.
High-Traffic Ecommerce
PDP + cart + checkout A/B. 0.5% CVR lift at scale = millions in annual revenue. Multiple tests running concurrently.
Product-Led SaaS
Signup, onboarding, activation, upgrade flows. Compound wins drive massive LTV + retention gains.
Consumer Apps + Social
Feature launches behind experiments, engagement metric optimization, recommendation algorithm tuning. Standard practice for mature consumer products.
Paid Media + Landing Pages
Ad + LP creative testing at scale. Multi-armed bandits ideal for paid-traffic-heavy brands running 10+ LPs simultaneously.
Model-agnostic, infra-rich, eval-first.
We run production AI on a deliberately diversified stack — so switching models or providers is a config change, not a rewrite.
WE SERVE YOUR INDUSTRY
Select Your Industry — Get a Custom Strategy
Click your industry below to start your free application — we'll tailor everything to your market.
Financial Services & Insurance
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Healthcare & Life Sciences
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Technology, Software & IT
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Retail & Ecommerce
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Real Estate & Construction
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Hospitality & Travel
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Automotive
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Manufacturing & Industrial
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Education & E-Learning
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Entertainment & Media
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Non-Profit & Government
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Logistics & Transportation
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Optimize while you sleep.
AI runs tests. Winners auto-deploy. Your CVR compounds monthly.
A/B testing pricing.
Starter
5-10 tests/mo
Growth
30+ tests/mo
Enterprise
Continuous optimization
A/B testing automation, answered honestly.
VWO / Optimizely vs. server-side testing?
VWO + Optimizely great for marketers running standalone page tests. Server-side testing (GrowthBook / Statsig) better for product teams wanting tests across web + mobile + backend. We recommend per team + use case.
How much traffic do I need to A/B test?
10K sessions / week minimum on tested pages for reasonable statistical power. Below that, tests take months or produce noise. Pre-launch CRO + qualitative research works better at low traffic.
What's a multi-armed bandit?
A testing method that auto-allocates more traffic to winning variants while still exploring. Faster learning than classic A/B splits. Best for optimization + paid traffic where velocity matters.
How do you prevent false positives?
Pre-registered hypotheses, power calculations before tests launch, sample-size locks (no stopping early), multiple-comparison corrections, Bayesian methods where applicable. P-hacking is testing malpractice.
How many tests per month?
5-15 well-run experiments / month for mid-sized teams. Quality > quantity — 3 well-powered tests beat 30 underpowered ones. Compound learnings matter more than test volume.
Three ways to get started
Pick the path that fits you best — a quick form, a detailed brief, or a live call. Selected service: AI & Automation.
Prefer phone? Call (480) 650-9911 — Mon–Fri · 9am–6pm MST