Test what your message actually lands as
Find out which claim persuades, which one triggers scepticism, and which word your audience reads differently from how you meant it — before you spend on distribution.
MiroFish tests messaging by simulating how different audience segments interpret it, not by scoring it. You supply the copy — a positioning line, an email, an ad, a launch announcement — plus who it is aimed at, and the engine models those segments as agents that react, object, and talk to each other. The report returns the interpretation each segment most likely takes away, which claim carries the persuasion and which invites doubt, the specific phrase most likely to be misread, and an estimated relative preference between variants. It measures interpretation, not conversion; only a live test measures conversion.
How MiroFish simulates it
Message testing works best comparatively, so the pipeline is built around variants rather than a single verdict:
- 1
Supply the copy verbatim and the audience
Paste the actual wording rather than describing it — the whole point is testing the words. Name the segments who will see it and, if they differ in sophistication or scepticism, say so. That difference is usually where messages break.
- 2
Segment-specific interpretation
Each audience group becomes an agent cohort with its own priors. A claim that reads as confident to a buyer often reads as overreach to a practitioner, and the simulation surfaces that split rather than averaging it away.
- 3
Reaction and objection rounds
Agents respond to the message and to each other, so objections compound the way they do in a comment thread. The objection that spreads is frequently not the strongest one, and that dynamic is what a solo read-through cannot show you.
- 4
Comparative interpretation report
Per-segment takeaway, the load-bearing claim, the phrase most likely to be misread, the dominant objection, and an estimated preference ranking across variants with reasoning attached.
A worked example
A dev-tools company tests two positioning lines: "Ship 10x faster" versus "Stop debugging deploy scripts", aimed at senior engineers and at the VPs who buy.
What you give it
- Variant A
- "Ship 10x faster"
- Variant B
- "Stop debugging deploy scripts"
- Segments
- Senior engineers, VP Engineering
- Channel
- Homepage hero + paid social
What the report estimates
- Engineer reading of A
- Marketing exaggeration, credibility drop
- VP reading of A
- Understood as directional, acceptable
- Preferred with engineers
- B — names a specific pain
- Dominant objection to B
- "Every tool claims this"
Illustrative output. Preference here is estimated interpretation, not measured click-through — validate the winner with a real test.
What it can’t do (honest limits)
- —It estimates interpretation, not conversion. A message that reads better may still convert worse for reasons of timing, placement, or offer.
- —It has no data on your specific audience’s history with you — a claim that is stale to your existing list may read as fresh to the simulation.
- —Tone and humour are the least reliable dimensions; irony in particular is frequently misjudged by simulated readers as it is by real ones.
- —It cannot replace an A/B test with real traffic. Use it to decide which two variants are worth the test budget.
Questions people ask
Can AI test marketing copy before launch?
It can test how copy is likely to be interpreted, which is the part most teams get wrong. MiroFish simulates named audience segments reading your actual wording and reacting to it and to each other, then reports the takeaway each segment forms, the claim doing the persuading, and the phrase most likely to be misread. It does not predict click-through rate — that requires real traffic.
How do I choose between two headlines?
Run both as separate simulations against the same segments and compare the reports. The useful output is not which one "wins" but why they diverge: often one performs better with buyers and worse with the practitioners who influence them, which is a trade-off you want to make deliberately rather than discover after launch.
Why does my messaging work with executives but not practitioners?
Because the same claim carries different risk for each. Executives read an ambitious claim as directional; practitioners read it as a factual assertion they can disprove, and a claim they can disprove costs you credibility with the exact people who evaluate the tool. Simulating both segments separately surfaces this split instead of averaging it into a single misleading score.
Can it tell me if my message sounds like AI wrote it?
It can tell you if your message sounds generic, which is the substantive version of that question. The report flags claims that could be made by any competitor — a common failure in positioning copy — and identifies where specificity would improve credibility. That is worth more than a style verdict.
What is the difference between message testing and A/B testing?
An A/B test measures what people did; message testing estimates what they understood. They answer different questions and the sequence matters: simulate first to narrow five candidate messages to two, then A/B test those two with real traffic. Testing five variants live splits your sample until nothing reaches significance.
Should I test a full email or just the subject line?
Both, separately. Subject lines are judged in isolation against an inbox full of competitors, while body copy is read by someone who has already opted in and has different scepticism. Simulating them together conflates two distinct decisions and usually produces vaguer output than testing each on its own.
Can it test messaging in another language?
The report is written in English, and testing translated copy is genuinely less reliable — idiom, register, and cultural priors are where simulated interpretation is weakest. Treat non-English results as a rough signal and get a native reader to confirm anything you plan to spend on.
How many message variants should I test at once?
Two or three per run, compared. Beyond that the differences become hard to attribute and the report gets less decisive. At a few dollars per run it is cheaper to do three focused comparisons than one sprawling one, and the focused output is more actionable.
Go deeper on the method: read the full guide on the blog →
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