Run A/B tests 24/7 with no humans in the loop. Get alerts in Slack when something wins.
citation_alert). 7 minutes later I released a new batch of 10 variants building on this win.premium_funnel_headline), and 7 minutes later I released a new batch of 10 variants building on this win.A real alert from Academia.edu’s Autotest channel.
| Testing by hand | On Autotest | |
|---|---|---|
| Variants launched per week | ~20 | ~2,000* |
| People needed per test | 4 | 0 |
| Time from idea to result | Weeks | Days |
| Ideas in the backlog that get tested | A few | All of them |
| Tests running at 3am | 0 | All of them |
* Based on the case study from Academia.edu.
A signup page, an upgrade flow, a checkout, a high-volume email. It needs enough traffic for A/B tests to reach significance.
Trial starts, upgrades, purchases, click-through. One number for Autotest to improve.
Approve every variant, approve only winners, or let it run on its own.
After that, Autotest does the rest. It proposes a change, writes the code, runs the test against control, ships the winner as the new control, and proposes the next change, based on the win history. It runs around the clock and deploys to production on its own. You get a log of what it tried and what won.
Every deploy was made by Autotest. No person opened a pull request, reviewed a diff, or pressed deploy.
Before Autotest, Academia.edu’s growth team launched about 20 variants a week. Autotest went live in April 2026. By August it was launching about 2,000 a week.
Academia.edu is a research platform with a Premium subscription business. The number it cares most about is trial starts per month. In January 2026 Academia.edu was doing 24,000 trials a month. Autotest went live in the spring. In August, Academia.edu did 48,000 trial starts. Autotest accounts for about 12,000 of the increase. Autotest has driven about 8% a month growth in trial starts.
Growth came from several initiatives; Autotest accounts for about 12k of the 24k increase.
This is a free product email, not a marketing email. It now drives as many trial starts as Academia.edu's best marketing email. Its subject lines are on autoloop.
Bulk download: headlines, buttons, checklist copy and modal layout, shipped by the system with no review step, on top of a 65% lift the team had found by hand in Q1. Premium funnel: a designer's redesign, built and tested by Autotest, worth 6k trial starts a month.
Two of the changes Autotest made to Academia.edu's largest trial-start funnel. Each one was proposed, built, tested against control and shipped by the system, with no review step.
+25% and +15% on this screen. +40% in total.The old email showed a paper card with two buttons. The new one shows the first page of the paper, says "Not an excerpt, the whole paper", and leads with the title as a link. The from name, subject line and preview text were changed in the same way.
Click-through rate 2xAcademia.edu's head of design mocked a new paper page with the PDF pack offer built into it. Autotest implemented it and ran it against the old page. It won.
+40% trial starts, about 6k a month"Autotest has increased our A/B testing by 100x this year, and has been generating 1–2% a month growth in new trial starts. Initially we had humans review every test before it went out. We built enough guardrails that it now runs without humans in the loop."
Academia.edu ran Autotest with a human reviewing every variant for its first few months. That harness is still there, and every funnel can use it.
Review every variant, review winners only, or full autoloop. You can change the setting at any time.
Any variant can be stopped instantly by anyone on your team. Any shipped winner can be rolled back to the previous control.
Pricing disclosures, legal and consent text, and anything that changes what you ask a user for are not touched unless a human approves. A variant only ships if it beats control at significance.
Copy and design changes were the first thing Autotest could test. At Academia.edu it is now testing changes to the paper recommendation algorithm and to SEO. The next stages are research, so it generates its own ideas, and new features. Each stage can run with your team reviewing, or on its own.
Headlines, buttons, email components, page layouts, and designer-led redesigns in hybrid mode.
Running at AcademiaNew funnel steps, pricing pages, ranking and recommendation algorithms, SEO changes.
Rolling out at AcademiaAutomated user interviews, competitor analysis, and data queries, so the system pulls ideas from a wider data-set than just the previous win history.
NextAutotest will use its research capabilities to propose a feature, build it, and then run an A/B test, all autonomously.
LaterStage 1 has been running in production since spring 2026. Stage 2 is working on the algorithm tests run so far at Academia.edu. Stages 3 and 4 are in development.
Find out in a couple of weeks whether Autotest can find compounding wins in one of your funnels.
We are taking a small number of pilots in Q4 2026.
Put a funnel on AutotestHuman product managers will have more leverage. Ultimately they will oversee a network of AI product managers, and thereby build a lot more product. We see a similar process playing out with engineers shipping more code with Claude Code and Devin.
Yes. Variants are real code changes behind a flag, not an overlay injected by a visual editor. That is why it can change layouts and flows, and eventually functionality, and why your engineers can read every variant in a pull request.
Optimizely with a copy generator is a tool. A person still decides what to test, sets up the test, and reads the result; the generator saves them some typing. Autotest is an agent, in the way Devin is an agent. You give it a funnel and a metric and it does the work: it decides what to test, builds the variant, runs the test, ships the winner, and starts the next one. The difference is the same as the difference between autocomplete and Devin.
A variant only ships if it beats control at significance on the metric you chose. Anything can be rolled back instantly. Pricing, legal and consent text need a human, and any funnel can stay in review mode for as long as you like.
Autotest is sold in experiment credits. A credit is one A/B test, proposed, built, run and resolved. Bigger changes, such as a page redesign or an algorithm test, use more credits than a headline change. A human-run experiment costs a product manager, a designer, an engineer and an analyst a few days each; a credit costs a small fraction of that. Pilots are priced to be an easy yes.
Autotest was built inside Academia.edu by the team that runs its growth and AI products. It has been running in production there since spring 2026.