Synthetic consumer simulation
Test your campaign on 1,000 synthetic consumers, before you spend a dollar.
Pebbles builds AI consumers from real, consented zero-party data. Simulate how a market responds to a product, price, or message, segment by segment, before the budget is committed.
Grounded in peer-reviewed choice science.
Predicted positive response by segment
0
Synthetic consumers per simulation
0%
Parity with human test-retest reliability
0%
Agentic task success rate
0.00
Segment stability (bootstrap Jaccard)
The problem
Brands spend first and learn second.
Traditional market research is slow, costly, and small-sample, so most launches fly blind and the real feedback only arrives once the media budget is already gone. Pebbles moves that learning upstream: simulate the market, then spend.
How it works
One loop: capture, simulate, activate.
Layer 1 · Capture
Showkey earns the data others guess at.
Showkey is an AI shopping assistant people actually want to use. In exchange for genuinely useful help, shoppers state real preferences, giving Pebbles consented first-party signal that behavioral tracking can never see.
- Zero-party data: stated, not inferred
- Consent-first, privacy-preserving capture
- Preference depth, not click exhaust
Showkey
What matters most for your next serum?
Zero-party · consented
Layer 2 · Simulate
1,000 AI consumers, shaped by real preferences.
Those preferences initialize a population of synthetic consumers. Pose a scenario, a new SKU, a price change, a campaign line, and a market-shaped response emerges segment by segment, in minutes instead of weeks.
- A population, not a single persona
- Segment-level response, not one blended average
- Minutes per scenario, run as many as you like
Scenario · price −10%
Layer 3 · Activate
Carry the winner into the real campaign.
The simulation does not replace the market, it tells you where to point before you pay for reach. Push the winning scenario downstream into live campaigns and commerce with MachimShop.
- Decide before the media spend
- Test messages, prices, and products
- From simulation to launch in one loop
MachimShop · recommended
“Fragrance-free. Absorbs in 30 seconds.”
Ready to launch
The engine
A simulation engine, not a chatbot wrapper.
Pebbles treats a large language model as a calibration input to choice science, conjoint and discrete-choice modeling, not as an oracle you ask and trust.
Grounded in real data
Synthetic consumers are initialized from consented zero-party preferences, not model imagination.
LLM as calibration, not answer
The model anchors and augments a choice-science core, so estimates stay consistent instead of drifting into bias.
A decision model underneath
Each consumer is a POMDP: belief seeded by Bayesian priors, anchored by RAG over real responses, explored with MCTS.
Private by design
Differential privacy (ε = 1.0) and k-anonymity at capture. No raw personal data enters the simulation.
Inside one consumer
Bayesian belief
seed priors from ZPD
RAG anchoring
ground on real responses
MCTS search
explore consumer actions
Aggregate
segment-level response
End to end
From a real preference to a market decision.
Capture
Consented zero-party preferences via Showkey.
Model
Build a belief state for each synthetic consumer.
Simulate
Run 1,000 agents against your scenario.
Predict
Aggregate a segment-level market response.
Activate
Hand the winning direction to your campaign.
Validated
Measured against human ceilings, not marketing math.
We do not predict the future. We reproduce how consumers actually respond, and we measure that against the reliability of human panels themselves.
Slot F1
90%
Preference extraction
Intent F1
90%
Intent classification
Response fidelity
85%
vs. human test-retest
Segment stability
0.85
Bootstrap Jaccard
Task success
85%
Agentic retail tasks
Targets are set at the human ceiling: matching the reliability of human panels, not claiming to beat it. Figures are internal targets, verified by an accredited test lab report.
Why Pebbles
Built to be trusted, not just impressive.
Grounded in real, consented data
Every synthetic consumer traces back to a real stated preference, captured with consent, not scraped or inferred.
Peer-reviewed science, not vibes
The method builds on published choice-science and generative-agent research, not a one-off demo trick.
Privacy engineered in, from capture on
Differential privacy and k-anonymity live in the pipeline by default, not bolted on after the fact.
Grounded in research
Standing on peer-reviewed work.
Pebbles is built on published science, not a proprietary black box. A few of the results the method relies on:
Fusing LLM output with real data is a consistent estimator: it cuts the sample needed by 24.9–79.8% at equal accuracy, while naive replacement adds bias.
Generative agents reproduce individual human survey and experiment responses at roughly 85% accuracy.
“Silicon sampling”: language models conditioned on real profiles act as statistically faithful proxies for human populations.
Human test-retest reliability itself sits near 90%, which reframes our 90% as human-ceiling parity, not a superhuman claim.
See it run
Simulate your market before you spend on it.
Tell us what you would test, a product, a price, or a message, and we will show you a Pebbles simulation on your category.
- ✓Zero-party by design, no tracking pixels
- ✓Your captured data stays yours
- ✓A working simulation, not slides