Weighted persona panel
Build synthetic users from your real audience mix: acquisition source, device, intent, price sensitivity, lifecycle stage, and category beliefs.
Calibrated synthetic A/B testing
Predict better A/B test winners before you spend traffic.
PersonaLab uses weighted AI personas, repeated model draws, historical human experiment calibration, and pilot gates to identify which variants deserve a real A/B test.
- 10-30x
- model draws per persona
- 4 gates
- kill, iterate, pilot, full A/B
- Human
- outcomes stay the source of truth
Variant ranking
Annual anchor beats weekly trial
+8.4%
predicted paid conversion lift
Calibration confidence
74%
Synthetic panel
18 weighted segments
- Paid social / skeptical21%
- Organic / high intent17%
- Price-sensitive trialer14%
- Relationship-driven12%
Decision gate
Run a 12% pilot
Similar to calibrated tests, but outside the strongest overlap region.
Kill
Iterate
Pilot
Full A/B
The problem
Raw LLM panels predict model behavior, not human behavior.
Recent research on LLM-based A/B testing shows the trap: if you ask a model which headline wins, you mainly estimate the causal effect on that model. It can be directionally useful, but it is not automatically a human lift.
PersonaLab treats LLMs as surrogate respondents that must be calibrated, stress-tested, and corrected with real experiment data.
Method
A surrogate lab with human outcomes as ground truth.
Repeated model draws
Query each persona many times per variant and aggregate the distribution, reducing single-sample noise and exposing disagreement.
Historical calibration
Learn the relationship between synthetic scores and real outcomes from past tests: CTR, signup, trial, purchase, retention, refund, and LTV.
Overlap diagnostics
Detect when a new idea is too far from the calibration set and downgrade confidence before the team over-trusts the forecast.
Workflow
From idea backlog to experiment decision in one pass.
Upload variants, choose the target audience, and connect your experiment history. PersonaLab returns predicted lift, segment-level objections, calibration confidence, and the next best action.
- Input Landing, ad, onboarding, paywall, email, or pricing variants.
- Simulate Run calibrated personas across clarity, trust, desire, and willingness to pay.
- Calibrate Map synthetic scores to your historical human experiment outcomes.
- Decide Kill weak variants, iterate unclear ones, pilot risky ones, A/B test winners.
Use cases
Built for teams where traffic is too expensive to waste.
Paywall optimization
Test trial framing, annual anchors, plan order, cancellation copy, and value proof.
Landing page positioning
Compare outcomes, objections, category language, social proof, and CTA intent.
Ad angle pre-tests
Screen hooks before spending on creative production or paid acquisition.
Onboarding funnels
Find where effort creates commitment versus where it creates friction.
Positioning
Not a replacement for A/B testing. A sharper filter before it.
Raw LLM vote
One model answer, no calibration, false precision.
Classic A/B test
Reliable, but expensive when every weak idea consumes traffic.
PersonaLab
Calibrated surrogate forecast plus pilot gates for uncertain cases.
Design source
Imagegen concept used as the initial visual direction.
The final page keeps the generated concept's restraint, white canvas, product-first hero, and single blue accent while replacing the mockup with production-ready HTML.
Start
Request demo
Bring your last 20 experiments. Leave with a calibrated test filter.
PersonaLab is for growth, product, and consumer AI teams that need fewer bad experiments and faster iteration loops.