Predictive population intelligence platform

Predict what will happen.
Understand why.

Simulate any population with social networks and temporal dynamics. Then interview individual agents about their reasoning. The only platform that combines quantitative simulation with qualitative interrogation.

Simulation Console

NYC Congestion Pricing · 500 agents · t=5/10

Position Distribution

Support
34%
Oppose
48%
Neutral
18%

Exposure: 78% · Avg conviction: 0.45 · Shares: 34

Network propagation active · 3 clusters identified

Interview: Maria Santos
Agent #042 · Oppose (Firm)

Why did you change your mind about the tax?

I was open to it at first — less traffic in Midtown sounded great. But when my neighbor Carlos showed me the cost breakdown — $15 a day, that's $300+/month — I reconsidered. Then Priya at work said she'd just avoid Manhattan entirely. It felt like punishing outer-borough commuters instead of fixing the subway.

What would change your mind?

How It Works

Three steps. Complete understanding.

No other platform combines quantitative simulation with qualitative interrogation and continuous calibration.

01

Simulate

Build a population. Run a scenario.

Describe your population in natural language. doxia.ai discovers attributes, grounds them in real statistics, samples agents, connects them in a social network, and simulates how they respond to your scenario over multiple timesteps.

Population Builder

"500 New York commuters, ages 25-65"

12 attributes discovered · Statistical grounding: strong

ageincomecommute_methodpolitical_leaning

Support: 34% | Oppose: 48% | Neutral: 18%

02

Interrogate

Interview any agent. Understand why.

Click on any agent in your results. Ask them why they made their choice. They respond grounded in their persona, their simulation history, the network influences they received, and their conviction trajectory. No other platform offers this.

Why did you switch to oppose?

"My neighbor Carlos showed me it'd cost $15/day. Then Priya said she'd avoid Manhattan. It felt like punishing outer-borough drivers."

Grounded in: 7 timesteps of simulation history, 3 peer influences, 2 position changes

03

Calibrate

Compare prediction vs. reality. Improve.

After implementation, upload real outcome data. See where the simulation was accurate and where it missed. The system recalibrates its models automatically. Every study makes the next one more accurate.

Predicted

48% oppose

Actual

52% oppose

Error

4pp

Model recalibrated. Next prediction: +12% accuracy.

The Killer Feature

Post-simulation interviews

See that 12% of your users would churn? Click on one. Ask why. They answer grounded in their persona, their simulation history, and the social influences they received. Nobody else offers this.

MS

Maria Santos

Agent #042 · Marketing Manager

Age34
Income$72,000
CommuteCar, 40 min
PoliticalModerate

Position Trajectory

NeutralNeutralOpposeFirm

Key Influences

CM
Carlos Mendezneighbor
PS
Priya Sharmacoworker

Say-Do Gap

Public: Firmly oppose

Private: Some environmental sympathy

Interview SessionGrounded in 7 timesteps of simulation

Why did you change your mind about congestion pricing?

Honestly, I was open to it at first. Less traffic in Midtown sounded great. But when Carlos next door showed me the actual cost breakdown — $15 a day, that's over $300 a month just for driving to work — I started to reconsider. Then Priya told me she'd just avoid Manhattan entirely, which would hurt the restaurants I love. It started feeling like the city was punishing outer-borough commuters instead of improving the subway.

Would a public transit credit change your position?

That's actually the kind of thing that would make me reconsider. If they offset the fee with a MetroCard credit, I'd feel like the city is investing in solutions, not just taxing problems. I know David from my online group supports it for exactly that reason — he takes the L train, so it doesn't hit him. But for drivers from Brooklyn like me, we need real alternatives first.

Ask Maria anything about her simulation experience...

Capabilities

Everything you need to understand your audience

Five layers of technology working together. From statistically grounded populations to deep agent interviews and continuous learning.

Layer 1

Statistical Population Grounding

Every attribute distribution sourced from real-world data — census, surveys, published research — with citations. Upload your own CRM or survey data for proprietary grounding.

Layer 2

Social Network Simulation

Small-world topology with weighted edge types (partner, coworker, neighbor). Conviction-gated sharing with flip resistance. Opinions propagate like they do in reality.

Layer 2

Two-Pass LLM Reasoning

Pass 1: agent reasons in character. Pass 2: structured outcome extraction. Eliminates central tendency bias that plagues single-pass approaches.

Unique

Say-Do Gap Modeling

Every agent has public and private positions. What they say vs. what they'd actually do. The gap where all the value lies in boycotts, politics, and pricing.

Layer 3

Post-Simulation Interviews

Chat with any agent after the simulation. They respond grounded in their full simulation history, network influences, and conviction trajectory.

Layer 1

RAG-Grounded Populations

Upload CRM data, past surveys, or interview transcripts. The system calibrates population distributions from your proprietary data instead of public statistics.

Layer 2

Temporal Evolution

Multi-timestep simulation with sliding-window memory. Watch attitudes shift over time as information propagates and agents update their beliefs.

Layer 4

Continuous Calibration Loop

Compare predictions against real outcomes. The system tracks accuracy by segment and recalibrates automatically. Every study makes the next one more accurate.

Validation

12 studies. 9 pass. Public artifacts.

The only reproducible public benchmark in the synthetic research market. Every spec, seed, and config is publicly available.

Pass

Netflix Password Sharing

Ground truth>80% retention
Prediction94.2%
Pass

Bud Light Boycott

Ground truth80-90% continued
Prediction85.8%
Pass

Spotify Price Hike

Ground truth95-98% retention
Prediction95.8%
Miss

NYC Congestion Pricing

Ground truth15-20% support
Prediction48.3%

+ 8 additional studies (6 pass, 2 miss). Full results and methodology at doxia.ai/blog

Use Cases

Every decision. Pre-tested.

Simulate first. Interview the results. Decide with confidence.

Pricing Strategy

Product Manager

Simulate 1,000 customers reacting to a 20% price increase. See 12% would churn. Interview the churners to understand what retention offer would keep them.

Public Policy

Policy Analyst

Model 5,000 constituents responding to a congestion tax. Track how opposition propagates through social networks. Interview swing voters to design better messaging.

Product Launch

Growth Lead

Simulate early adopter networks for a new feature. Identify which user segments become evangelists and which become detractors. Interview each to optimize onboarding.

Campaign Messaging

Political Strategist

Test 3 different campaign messages on simulated voter populations. See which creates the most persuadable movement. Interview undecideds to understand what resonates.

Crisis Response

Communications Director

Simulate how a product recall propagates through customer networks. Identify high-influence critics. Interview them to design response messaging before the crisis hits.

Market Research

Strategy Consultant

Replace a $50K focus group with a 2,000-agent simulation. Get distributional outcomes, network dynamics, and deep interviews — in hours instead of months.

Pricing

10-50x cheaper than traditional research

A $50K focus group or a $199/month simulation with deeper insights. You choose.

Starter

Free

For individual researchers and evaluators

  • 5 studies/month
  • 100 agents max per study
  • 3 timesteps max
  • 5 agent interviews/study
  • Community support
Get Started Free

Team

$499/month

For mid-market teams and agencies (up to 5 seats)

  • 50 studies/month
  • 5,000 agents per study
  • Unlimited everything
  • Batch interviews
  • Compare studies
  • Team collaboration
  • Priority support
Start Team Trial

Enterprise

Custom

For large consultancies and policy organizations

  • Unlimited studies
  • 50,000+ agents
  • SSO / SAML
  • SOC 2 compliance
  • Dedicated infrastructure
  • Custom model training
  • White-label available
  • Dedicated CSM
Contact Sales

All plans include population simulation, social network modeling, and temporal evolution. Higher tiers add interviews, RAG, calibration, and team features.

Full Transparency

Open methodology. Auditable results.

Every simulation is fully inspectable. Population specs, attribute distributions with sources, agent reasoning traces, network propagation paths, and conviction trajectories. No black boxes. Every result can be audited and reproduced.

Learn About Our Methodology
What You Can Inspect
Population specs with statistical sources for every distribution
Full agent profiles with all sampled attributes
Network topology and connection rationale
Per-timestep reasoning traces for every agent
Propagation paths showing who influenced whom
Conviction trajectories and say-do gap analysis
Reproducible results with deterministic seeds

Stop guessing. Start simulating.

doxia.ai is launching in 2026. Get early access to the platform that predicts how populations respond to any scenario — then lets you interview them about why.

Simulate populations. Interview the results. Calibrate with reality.