Feature name was opaque
Before
Named the new feature 'Pulse'.
After
ICP guessed 6 different things. Renamed 'Live Reactions'. Comprehension +71%.
Name was abstract, not descriptive.
Feature copy check
The check before you name the feature.
Paste a feature name and description. 500 ICP buyers tell you what they think it does, whether the name conveys the value, whether they'd click it, and what they'd call it instead. In 60 seconds.
No card. No signup. Three free checks in your first minute.
How the check runs
Four steps. 60 seconds end to end.
/ 01
Paste
Feature name plus a one-paragraph description. Or three name variants for A/B/C.
/ 02
Pick an audience
Same audience that'll see the changelog, launch tweet, or in-app announcement.
/ 03
Run
500 ICP buyers describe what they think it does, rank the names, pick a variant.
/ 04
Read the report
Predicted comprehension, name preference, alternative names suggested by buyers.
What you get back
A real report, not a vibe check.
Verdict, sentiment distribution, verbatim quotes from 500 simulated ICP buyers, the most common objection, friction points, recommendations. One artifact you can paste into your team Slack and act on tomorrow.
Feature copy check · sample
Run #04219 · 500 buyers
Positive
58%
Neutral
18%
Negative
24%
Top objection
“I don't see a price anywhere, feels like enterprise sales.”
full report
What founders catch
Three real examples of what the check found.
Launch tweet hook was a feature, not a benefit
Before
Tweet led with 'now with custom audiences.'
After
64% read it as a feature. Rewrote as 'know what your buyers actually think, before you ship.' #2 on Product Hunt.
Hook wasn't framed for the buyer.
Changelog didn't explain why
Before
Changelog: 'Added support for X.'
After
ICP didn't know why they'd care. Rewrote with one-line use case. Engagement +52%.
What-not-why phrasing.
Common questions
Things SaaS founders ask before running a check.
Is this just GPT?+
No. Every check runs through nine independent corrections: a multi-model ensemble across multiple independent frontier model families, calibration against historical ground truth, revealed-preference weighting, and distribution-shape matching. One model wrapped in a persona prompt is one model's opinion. We give you 500.
How accurate is this?+
87% median accuracy across calibrated SaaS clusters, audited monthly. Every cluster is dated, sourced, and visible on the validation page. If a cluster drifts below 80%, we pause it automatically.
What audiences are available?+
Pre-built clusters for B2B SaaS buyers (SMB and mid-market), indie hackers, dev-tool buyers, marketing-led SaaS, sales-led SaaS, PLG users, agency owners, and API-first buyers. New clusters land monthly. See the audiences page for status and accuracy.
Does it work for B2C?+
Today the calibrated SaaS clusters are the focus. The same engine powers our enterprise customers' B2C work, see the enterprise page for that. If you're a SaaS founder targeting consumers, the indie-hacker and product-led clusters are the closest fit while we calibrate B2C-specific SaaS audiences.
Test your feature copy free.
No card. No sales call. Three live reactions in your first minute.