You're right to be skeptical.
AI-generated audience responses require skepticism. Here is how we structure inputs, add reference context, and expose limitations so you can decide what to validate with real customers.
Illustrative demonstration — not a measured customer result
Model-Alignment Report
Sample data — simulated persona panel
How to interpret AudiAInce® output
The standard panel uses 41 predefined AI audience archetypes. Responses and scores are model-generated and directional; they are not human respondents, statistically representative samples, or guarantees of market performance. Where scenario simulations use repeated mathematical iterations, those iterations are not additional agents, people, impressions, or survey responses. Validate material decisions with human research, live experiments, or observed market outcomes.
The problem with "correcting" AI opinions
Traditional approach
Most synthetic research uses statistical priors to "correct" AI responses toward expected distributions. If the AI says 60% would buy but you expected 40%, adjust it down. This destroys the signal you're paying for.
Our approach
We do not treat model responses as human observations. Where reference statistics are available, the report can show model alignment or divergence for context; this does not establish representativeness.
Why this matters
Irrational consumer behavior is valuable data. When our "Budget Mom" persona unexpectedly loves your premium product, that's a signal - not an error to smooth away. You want the insight, not a sanitized average.
41 personas. Not 41 stereotypes.
Each persona is a designed AI audience archetype with demographic, behavioral, and psychographic attributes. These profiles create distinct model perspectives; they are not real people or human respondents.
Core Demographic
Personas built on the life stage x income matrix. From budget-conscious urban starters to affluent empty nesters - the backbone of any consumer panel.
- Young singles across income tiers
- New parents navigating first purchases
- Established families balancing budgets
- Empty nesters with disposable income
Psychographic
Personas defined by how they buy, not who they are. These archetypes cut across demographics - a deal hunter can be 25 or 55, male or female.
- Tech early adopters seeking novelty
- Skeptics who research everything
- Values-driven sustainability buyers
- Emotional impulse purchasers
Edge Cases
Contrarian voices that break assumptions. The wealthy person who clips coupons. The marketer who sees through everything. The voices that challenge your positioning.
- High-income but value-obsessed
- Time-starved single parents
- Industry insiders immune to marketing
- Extreme deal hunters
What makes an archetype distinct
Each persona includes 10+ attributes that create consistent, believable behavior across every interaction:
Identity
- Demographics (age, gender, income)
- Life stage and current situation
- Financial constraints and goals
Psychology
- Decision-making patterns
- Core values (ranked by priority)
- Emotional triggers and fears
Behavior
- Platform and media preferences
- Purchase turn-offs and red flags
- Typical objections raised
Reference comparison for model responses
Where reference statistics are available, calibration questions can compare selected model responses with those base rates. This shows model alignment or divergence; it does not establish that the AI panel represents human behavior.
Generate Metrics
Claude analyzes your product and identifies demographic or behavioral statistics that may provide useful reference context for the use case.
Search Base Rates
Perplexity API searches authoritative sources in real-time, returning specific statistics with citations. No stale data - current numbers from government and academic sources.
Add Model-Alignment Questions
Corresponding questions are added to your survey automatically. These measure the AI panel's characteristics using the same methodology as the base rate sources.
Compare & Score
Where reference statistics are available, selected AI archetype responses can be compared with those base rates. Any model-alignment score is an internal diagnostic; review the cited source and configured weighting.
Transparent Reporting
Reports can show an alignment score, dimensions that aligned or diverged, available source citations, and a plain-English interpretation.
Compared with available reference data
Reference searches may return government, academic, polling, industry, and other published statistics. Review each citation and methodology before relying on it.
Available sources can contribute differently to an internal alignment diagnostic. Review each citation, methodology, and configured weighting before relying on the comparison.
Personas remember your brand
Brand Memory carries approved brand context between model sessions so AI persona responses can remain consistent with earlier supplied information. It does not reproduce human memory or behavior.
Brand Memory: Budget Mom
Sample data — simulated persona panel
Longitudinal consistency without averaging
Each AI persona retains its defined perspective and approved brand context across sessions. Model responses may change as that supplied context changes.
- Persistent memory across surveys and interviews
- Opinion evolution based on new information
- Relationship tracking over time
- Consistent personality, not random variation
Model diagnostics included
Survey summaries include descriptive statistics and simulation diagnostics for model-generated responses.
Descriptive statistics
Mean, median, standard deviation, and model-simulation ranges summarize how AI archetype responses cluster or scatter. They do not measure uncertainty in a human population estimate.
Purchase Intent
Sample data — simulated persona panel
Response distributions
See the full shape of responses - not just averages. NPS-style breakdowns reveal promoters, passives, and detractors at a glance.
Intent Distribution (NPS-Style)
Sample data — simulated persona panel
Segment comparison
Automatic breakdown by demographic segment with model-internal indicators for differences worth investigating in real-customer research.
Segment Comparison
Sample data — simulated persona panel
Executive summary
AI-generated key insights with priority levels and segment tags. Ready for stakeholders who won't read the full report.
Key Insights
Sample data — simulated persona panel
What we don't claim
Not a replacement for validation
AI research is for exploration and hypothesis generation. Before you bet the company on a direction, validate with real humans. We're the first conversation, not the final word.
Not perfect representation
AI archetypes are not a human sample and do not represent every variation in a market. Use human research for population claims and custom definitions only as additional model perspectives.
Not immune to AI limitations
LLMs can be wrong. Review model inputs, responses, simulation diagnostics, and available citations, then validate consequential findings externally.
See the methodology in action
Run a free survey and review the model-alignment report, simulation diagnostics, and limitations. Compare the output with evidence you already trust before using it for a decision.