HiveSight lets marketers, product teams, campaigns, and researchers ask synthetic audiences the same questions they would ask human respondents. Start with geography, read the calibrated population underneath it, and act on the result.
Use it for
Testing messages, products, positioning, and targeting decisions without waiting weeks for fieldwork.
Geography-first
Target a ZIP code, state, district, or national audience and compare where the same claim lands or breaks.
Scales with the question
Start with a free run, then scale sample size and model quality when the decision needs more precision.
Richer than location
Each run is grounded in income, occupation, housing, family, disability, insurance, and benefits data.
Free to try. No account required. Three runs per day with 25 respondents each, then larger hives and paid models when you want more precision.
Free access
Use the full workflow free. Sign in only when you want larger samples, saved work, or paid models.
Live research desk
Start with a free 25-person Likert run. Scale sample size and model strength when the question calls for more precision.
Good for
Messages, concepts, product ideas, pricing language, customer questions, and segment strategy.
What to watch
When the answer changes by place or subgroup, that is a targeting insight, not a warning label.
Audience model
Geography is the clearest first cut. But each run starts from a geography-assigned calibrated population built from household, demographic, and socioeconomic microdata, so the output is more than a map pin.
Catch neighborhood effects where rent burden, commute patterns, and local identity reshape the same line.
Compare statewide framing when the coalition is broad and the household mix shifts across metros, suburbs, and rural places.
Read a congressional seat through constituent composition, not just party label or topline vote share.
Under the hood
HiveSight starts by place because that is the fastest real question to ask. Underneath, it runs on a calibrated local population instead of bolting local weights onto a generic audience after the fact.
Method
The goal is usable signal strong enough to make real decisions, fast enough to keep up with the work.
Why it matters
If a line only works in one place or one slice of the audience, that is not noise. It tells you where the strategy, offer, or message holds and where it breaks.
Start with the claim, concept, headline, product description, or question you want to pressure-test.
Start with ZIP code, state, congressional district, or the full US. HiveSight switches to the calibrated population for that place rather than reweighting a generic national sample after the fact.
Inspect place-level shifts, audience composition, and raw responses to see where the local population actually changes the result.
Output
You get enough structure to decide what is working, what breaks, and which parts of the audience are driving the result.
Get the fast read first so you can see whether the message is broadly working or failing.
Read individual answers in context instead of treating the output like a single summary blob.
See the mix by age, sex, race/ethnicity, occupation, housing, insurance, and benefits context.
Change the line or the geography and rerun quickly before you move into expensive research.
Use it to decide
Use it for messaging, marketing, product, editorial, and targeting decisions. The strength is direct inference on a calibrated local population, not a generic persona layer with geography sprinkled on top.
Where it helps
Message testing before ad spend, launch, or campaign rollout.
Concept and feature research before roadmap or creative commits.
Segment and geography calibration before full distribution.
Next move
Many questions can be answered here. When you want more confidence, larger hives and paid models are already in the same workflow.
Target ZIP codes, states, districts, or the full US.
Start free and scale sample size or model quality as needed.
Inspect audience composition and raw responses grounded in a calibrated local population.