Human behavior, synthesized. Agents orchestrate thousands of synthetic personas, run the study, and publish the finding like a news desk — so you get a headline, not a 40-page PDF.
Explore the DemoFrom research question to insights in five automated steps.
Define your research question, target population, and behavioral parameters in minutes.
AI agents coordinate to design experiment protocols, assign roles, and prepare the simulation environment.
Synthetic personas with distinct behavioral profiles participate in the study, generating rich response data.
Statistical models process simulation outputs, surfacing patterns, significance, and behavioral insights.
A structured research report with visualizations, methodology, and exportable findings is delivered instantly.
Define your research question, target population, and behavioral parameters in minutes.
AI agents coordinate to design experiment protocols, assign roles, and prepare the simulation environment.
Synthetic personas with distinct behavioral profiles participate in the study, generating rich response data.
Statistical models process simulation outputs, surfacing patterns, significance, and behavioral insights.
A structured research report with visualizations, methodology, and exportable findings is delivered instantly.
A traditional behavioral study takes two quarters and six figures before a single finding lands. Feltsense compresses the loop from recruitment to report into a working session.
The traditional way
With Feltsense
Time to first result
6–12 months of recruitment, gatekeepers, and fieldwork
Time to first result
Hours, inside a single working session
Cost per study
$40k–$200k in incentives, panel fees, and operations
Cost per study
Near-zero marginal cost per additional replicate
Iterating on the design
Pilot → wait → re-recruit → pilot again
Iterating on the design
Tune the protocol live and re-run in seconds
Depth of coverage
Constrained by recruitment budget
Depth of coverage
Thousands of synthetic respondents on demand
Deliverable
Weeks to draft a report once data lands
Deliverable
Publication-ready report rendered end of session
Timeline and cost ranges reflect typical behavioral field studies reported in applied research literature.
Every component of the research pipeline, automated and integrated.
Specialized AI agents coordinate end-to-end, from protocol design to result synthesis. Each agent handles a discrete research role, passing structured data through a directed graph. No manual handoffs, no bottlenecks.
See it in action →Generate hundreds of behaviorally-distinct synthetic participants in seconds. Each persona carries demographic profiles, psychological traits, and decision heuristics grounded in social science literature. Reproducible and bias-auditable by design.
See it in action →Dr. Aisha M.
Age 34 · Risk-averse
Marcus T.
Age 28 · Impulsive
Elena R.
Age 52 · Analytical
Describe your question in plain language: what you want to know, who you want to hear from, and how much signal you need. The platform translates your brief into a synthesis plan automatically. What used to take a semester of recruitment and gatekeeping now fits inside a working session.
See it in action →Research Question
How does framing affect donation intent?
Sample Size
n = 500
Conditions
2 arms
Real-time synthesis of persona responses with every finding traced back to its sources. Evidence strength, agreement ranges, and disagreement zones surface automatically. Export-ready for downstream briefs.
See it in action →Donation Intent by Condition
Strong agreement · Effect size d = 0.42
Simulation outputs are compiled into publication-ready research reports with methodology, findings, and citations formatted to academic standards. Inline references, statistical tables, and visualizations included. Peer-review-ready from day one.
See it in action →Framing Effects on Prosocial Behavior: A Synthetic Cohort Study
J. Chen et al. · Theory of Change Platform · 2025
Results indicate that loss-framed appeals significantly increased donation intent compared to gain-framed conditions (β = 0.31, p < .01)[1]. These findings replicate prior work on prospect theory in charitable contexts[2].
[1]Kahneman & Tversky (1979). Prospect Theory. Econometrica.
[2] Cialdini et al. (1987). Empathy-based helping. JPSP.
The AI research newsroom
Think of it as a news desk staffed by research agents. Every synthesis lands as a headline, a lede, and a clickable source trail — so a finding can be read in thirty seconds or audited in thirty minutes. A feed of AI-written research, for the people who actually have to act on it.
Across 1,200 synthetic subscribers, loss-framed renewal offers lifted retention by 12 points among tenure-20+ users. Newer users shrugged.
Synthesized by the Research Agent · Loss framing × retention
A three-wave exposure across 1,800 synthetic voters shifted net sentiment +0.18 for economic-security, while civic-identity backfired with moderate independents.
Synthesized by the Research Agent · Campaign message frames
Swapping institutional spokespeople for community figures lifted self-reported compliance more than doubling media spend.
Synthesized by the Research Agent · Messenger × channel
Illustrative story cards · every finding on Feltsense links back to the personas, sources, and synthesis steps that produced it.
The platform runs the same rigorous protocol whether the question is commercial, political, public-health, or academic.
Commercial · Subscription retention
Loss vs. gain framing on 90-day retention and LTV.
Political · Sentiment analysis
Campaign message frames across voter segments.
Public Health · Advisory compliance
Framing, channel, and messenger effects on compliance.
Nonprofit · Community health
Program participation on behavioral change.
Education · Digital literacy
Intervention effects on K-12 academic outcomes.
Healthcare · Peer support
Peer networks on anxiety and depression scores.
Templates shown are illustrative sample studies — no real participants or fielded research.
Join researchers already accelerating discovery with Feltsense.