Monthly betas used to eat six weeks of chasing; now the whole cycle runs itself. Power users are invited automatically, NDAs signed, access provisioned, feedback collected on schedule — and an AI findings report lands the day the beta closes.
Typical results
73%
Feedback completion rate
3 hrs
PM time per beta, was a week
Same day
Findings report delivered
Figures reflect typical results for this kind of automation.
The challenge
Every monthly beta meant a product manager hand-picking testers, chasing NDAs over email, provisioning access one account at a time and begging for feedback that arrived late or never. Cycles stretched to six weeks, findings landed after the release decision, and each beta stole roughly a week of PM time.
Before and after
Beta cycle length
−71%Feedback completion rate
2.4×PM hours per beta
−93%The automation
We built a Make workflow that runs the entire program. Usage data selects power users matching the target profile and invites them automatically; NDAs go out for e-signature, and beta access is provisioned the moment each one is signed. Structured feedback forms fire on a schedule, with reminders chasing silence. When the beta closes, OpenAI clusters every response into themes, severity and standout quotes — a ready findings report.
How it works
- 01
Right testers invited
Usage data selects power users matching the target profile and sends invites automatically.
- 02
NDA and access
E-signature requests go out; beta access is provisioned the moment each NDA is signed.
- 03
Feedback on schedule
Structured forms fire at set checkpoints, with reminders chasing anyone who goes quiet.
- 04
AI clusters the responses
OpenAI groups every answer into themes with severity ratings and standout quotes.
- 05
Report on close day
The findings report lands with the product team the day the beta ends.
Starts from
- Power-user segment
- Product usage signals
Delivers
- NDA signed, access granted
- Scheduled feedback forms
- AI findings report
The impact
Beta cycles dropped from six weeks to twelve days, and feedback completion jumped from 31% to 73% of invited testers. The findings report now lands the same day a beta closes — in time to shape the release — and product managers spend three hours per beta instead of a full week.
Questions about this automation
How are beta testers selected?
The automation reads product usage data and picks accounts that match the profile set for each beta, such as heavy users of a certain feature or customers on a specific plan. Selected users receive an invite automatically, and the product manager can cap or review the list before invites go out.
What happens between the invite and the first feedback form?
Users who accept receive an NDA for e-signature. The moment it is signed, beta access is switched on for their account through the product's admin API or a feature flag. Feedback forms are then sent at set checkpoints, and reminders follow anyone who has not replied.
How does the AI turn responses into a findings report?
When the beta closes, OpenAI reads every form response and groups the answers into themes, each with a severity rating and representative quotes. The product team receives the report as a document or a shared sheet, and every theme links back to the raw responses so the findings can be checked.