Customer language
Interview notes, review snippets, survey answers, sales calls, support tickets, and objections in the words customers actually use.
Customer reaction rehearsal
Customer Research Simulation AI
Turn customer interviews, support notes, reviews, and product ideas into simulated reaction rounds before you ship a change or run another research cycle.
Built for scenario planning, not certainty. Every output should be reviewed against real evidence before an operating decision.
Scenario tape / illustrative
Customer research simulation for a product change
R1
Power usersWorkflow concern emerges
R2
New usersValue language clarifies
R3
SupportConfusion path appears
Dominant risk
Misread need
Pressure source
Segment conflict
Evidence gap
Workflow proof
Input
Interviews, reviews, support notes
Engine
Segment reaction graph
Output
Needs, objections, follow-up questions
What to bring
Bring messy customer evidence into one reaction surface.
Customer research simulation works best when the source includes real user language, interview notes, support tickets, review excerpts, product constraints, and the decision you are testing.
Product decision
The feature, message, onboarding change, workflow change, or package change you want to test.
Segment context
Who the users are, what they already tried, where they get stuck, and what each segment values differently.
Where it earns its keep
Use simulation to decide what research should happen next.
Customer research often ends with more questions. MiroFish helps teams find which questions matter before the next interview or experiment.
Feature idea01
Test whether the problem is strong enough.
Simulate how different user segments react to the idea and where the claimed value feels weak.
Onboarding change02
Find the step that creates confusion.
Model how new users, power users, and support teams interpret a changed workflow.
Research synthesis03
Turn scattered notes into testable assumptions.
Surface contradictions, missing context, and questions that deserve validation.
Customer research is a map of conflicting incentives.
MiroFish turns customer evidence into segments, needs, objections, memory, and simulated responses so product teams can inspect the decision before shipping.
Stage 1
Ground the scenario
Upload source material and define the decision, event, or message you want to test.
Stage 2
Build the actor graph
Map the people, groups, incentives, constraints, and memory that shape the reaction.
Stage 3
Run reaction rounds
Let the simulated actors respond over multiple rounds so the second-order path appears.
Stage 4
Question the report
Review the trajectory, risks, weak assumptions, and what evidence would change the conclusion.
What the report should answer
The report should improve the next product decision.
- Which customer segment reacts most strongly
- Which need is real versus assumed
- Which objection may block adoption
- Which support or onboarding gap may appear
- Which research question should be asked next
Boundary conditions
Not a replacement for real customers.
- Not a substitute for interviews, analytics, usability tests, or experiments
- Not a guarantee of adoption or retention
- Not useful when no real customer evidence is provided
- Not a demographic survey panel
Why MiroFish
Customer research simulation should make assumptions inspectable.
Generic AI can summarize notes. MiroFish models how different customer segments may react when a real product decision changes the situation.
Research synthesis
Generic AI
Theme summary
MiroFish
Segments, needs, objections, and reaction paths
Decision risk
Generic AI
Advice list
MiroFish
Simulated adoption and confusion pressure
Next research
Generic AI
General questions
MiroFish
Evidence gaps tied to the specific decision
FAQ
Customer research simulation AI, without pretending to replace customers.
Use it as a rehearsal layer between raw evidence and the next research or product decision.
What is customer research simulation AI?+
Customer research simulation AI uses customer evidence to model how different segments may react to a product idea, message, feature, or workflow change.
Can it replace interviews?+
No. It should help decide what to ask next, where assumptions are weak, and which objections deserve validation with real users.
What should I upload?+
Upload interview notes, support tickets, customer reviews, survey answers, sales notes, product specs, onboarding flows, or messaging drafts.
Who should use it?+
Product managers, founders, researchers, marketers, and customer success teams can use it before shipping or testing a customer-facing change.
What makes the output useful?+
The output is useful when it points to concrete assumptions, segment differences, objection paths, and follow-up questions your team can validate.
Related simulation paths
Follow the risk into the next decision.
Most decisions do not stay inside one category. These adjacent use cases help teams test the next market, customer, or public reaction path.
Buyer response
Buyer Reaction Simulation
Simulate how buyers interpret messages, pricing, proof, switching risk, and competitor alternatives.
Open Buyer Reaction SimulationLaunch reaction
Product Launch Simulation AI
Test buyer understanding, competitor framing, and the first market story around a launch.
Open Product Launch Simulation AIPricing rollout
Pricing Simulation AI
Rehearse churn narratives, package migration risk, and pricing objections before rollout.
Open Pricing Simulation AIResearch creates choices
Turn customer evidence into a decision rehearsal.
Bring interviews, support notes, reviews, or product ideas. MiroFish will turn them into a customer reaction scenario you can inspect.
Start a customer simulation