Research prospectus
Decision-grade audience research grounded in calibrated US microdata.
Every claim in this document is corroborated with a primary source. Hover over citations to see details. Click to open.
Traditional survey research is slow, expensive, and increasingly unreliable.
Professional panels cost $5-50 per response. A 1,000-person survey runs $5K-50K.
Weeks to design, field, and analyze. By the time data arrives, the moment has passed.
Survey fatigue, bots, and inattentive respondents degrade data quality.
The market research industry generates $140 billion annually[6], yet the core methodology—asking humans questions—hasn't fundamentally changed in decades. Panel providers struggle with response rates, quality control, and the inherent latency of human recruitment.
"Within three years, more than half of market research may be done using AI-created synthetic personas instead of humans."— Qualtrics 2025 Market Research Trends Report[2]
The shift is already happening: 69% of market research professionals have used synthetic data in the past year, and 87% report high satisfaction with the results.[3]
The breakthrough is not just that LLMs can mimic survey respondents. It is that once you have a calibrated synthetic population, you can run direct audience inference on it. The lineage runs from silicon sampling[4] through MRP-style local estimation[24] and spatial microsimulation[25].
The strongest systems combine a response model with real population structure. That is why small-area estimation uses MRP and why spatial microsimulation builds explicit local synthetic populations. HiveSight applies the same logic with LLM respondents over calibrated local microdata.[24][25]
Demonstrated that GPT-3 conditioned on demographics reproduces voting patterns, policy preferences, and social attitudes that match ANES and Cooperative Election Study data.
Showed that LLMs can predict treatment effects in large text-based survey experiments, which is directly relevant to message testing and framing decisions.
Reviewed silicon sampling in consumer research, finding strong promise for pretesting and pilot studies, with specific recommendations for main study use.
Models perform better for Western, Educated, Industrialized, Rich, and Democratic populations due to training data distribution.[15]
Simulated sample sizes below 200 can produce unreliable or reversed results.[20]
Synthetic sampling can stand on its own for many message-testing and iterative research workflows, while novel topics and the highest-stakes decisions may still warrant extra validation.[18]
HiveSight turns silicon sampling into a real research product. Ask a question, start with a geography, and get decision-grade responses grounded in an audience model built from calibrated US microdata. Geography is the entry point, not the only dimension.
The strongest early critique of synthetic survey respondents was not that LLMs can never be useful. It was that lightly prompted personas often recover averages while missing the variance, subgroup structure, and coefficient stability researchers care about.[21]
That critique matters, but it is not the right benchmark for HiveSight. Classical MRP estimates local opinion by fitting a response model and then post-stratifying over cell counts.[24] Spatial microsimulation takes a different route: it builds explicit small-area synthetic microdata and uses that richer local population for inference and policy analysis.[25]
HiveSight is closer to the second tradition. The calibration happens upstream when we construct a geography-assigned synthetic population. At run time, we filter the user's target audience and simulate responses directly over that local population instead of reweighting a generic national sample after the fact.
More recent work suggests this richer setup matters. LLMs perform substantially better when the task is benchmarked directly on text-based treatment effects[22] and when simulated respondents are grounded in much richer descriptions than demographics alone.[23] For HiveSight, the relevant question is therefore not “can a generic LLM impersonate a survey respondent?” but “does direct inference on calibrated local synthetic populations improve subgroup and place-level fidelity on real audience-research tasks?”
For many messaging, marketing, product, editorial, and targeting workflows, HiveSight is not just the pretest. It is the research layer teams can use to make the call.
5-point agree/disagree scales. Visualize distributions and calculate statistics.
4 respondents per credit (GPT-5 Mini)Free-form responses with reasoning. Qualitative insights at scale.
2 respondents per credit (GPT-5 Mini)ZIP codes, districts, states, national
Age, income, occupation, housing tenure, family structure
Race/ethnicity, disability, insurance, benefits, student status
We sit at the infrastructure layer beneath multiple large and growing markets.
7.7% CAGR through 2030. Survey research, focus groups, and opinion polling.
Synthetic respondents and AI-driven insights growing 15%+ annually through 2035.
PhD students, postdocs, and faculty with limited budgets but need for survey data.
Rapid iteration on messaging, feature prioritization, and user sentiment testing.
83% of market research professionals plan to invest in AI for research in 2025.[6] 64% of researchers increased AI tool usage in 2025.[13]
Traditional survey platforms haven't adopted AI respondents. Research tools lack web UIs.
| Capability | Qualtrics | SurveyMonkey | EDSL | Pollfish | HiveSight |
|---|---|---|---|---|---|
| AI respondents | — | — | ✓ | — | ✓ |
| Web UI (no code) | ✓ | ✓ | — | ✓ | ✓ |
| Instant results | — | — | ✓ | — | ✓ |
| Low cost per response | — | — | ✓ | — | ✓ |
| Audience targeting | ✓ | ✓ | ✓ | ✓ | ✓ |
Acquired for $12.5B. Human panels only. Enterprise pricing.
$500M revenue. Audience feature uses human panels.
Python library. Research-focused. No web UI.
$5.8M revenue. Mobile-first human panels.
Hybrid model: credits + subscriptions
The AI SaaS industry is moving toward hybrid pricing.[7] 39% of SaaS companies now use usage-based pricing, and 22% use hybrid models.[8]
Industry insight: "Start with usage-based or prepaid credits to reduce friction, then evolve toward hybrid or subscription models as engagement increases."[8]
| Company | Model | Rationale |
|---|---|---|
| OpenAI[12] | Hybrid | $20/mo Plus OR pay-per-token API |
| Midjourney | Hybrid | Subscription tiers with GPU credits |
| GitHub Copilot | Subscription | $19/seat flat, encourages adoption |
Pay for what you use. No commitment.
1,000 credits/mo for regular users
10,000 credits/mo for power users
Founder & CEO
Co-founders & early team
Interested? max@hivesight.ai
"Won't people question AI-generated data?"
We're transparent about methodology. Position as rapid prototyping and hypothesis generation, not replacement for high-stakes research. Validation studies show strong correlation with human data for many use cases.[1]
"Models underrepresent non-Western views"
Focus initial launch on US market where training data is strongest. Clear documentation of limitations. Future: custom fine-tuning for specific populations.[15]
"What if OpenAI raises prices?"
Model-agnostic architecture. Can switch between providers (OpenAI, Anthropic, open source). Pricing already includes margin for cost increases.[12]
"Qualtrics could add this feature"
Enterprise incumbents move slowly. Their business model depends on human panels—AI respondents cannibalize revenue. We're purpose-built for AI-first research.[9]
"Why no co-founder yet?"
Actively hiring. Seed capital enables founding engineer hires. Strong advisor network from PolicyEngine and Cosilico.
"Researchers don't trust AI data"
Industry adoption is accelerating—83% plan AI investment in 2025.[6]Academic publications legitimize the methodology. Start with early adopters.
| Year | ARR | Customers | Milestone |
|---|---|---|---|
| Y1 | $100K | 50+ | Product-market fit, first enterprise pilot |
| Y2 | $500K | 200+ | API launch, 2-3 enterprise deals |
| Y3 | $2M | 500+ | Enterprise sales, international expansion |
| Y4 | $5M | 1000+ | Platform status, academic partnerships |
| Y5 | $15M | 2500+ | Category leader |
We're building the future of survey research.