Most tools build a separate model for every trait they measure. Intelafy measures the whole population directly instead, with one engine and one scale for hundreds of millions of people and more than 100,000 traits. Because the measuring is already done, the same question gets the same answer: between scheduled recalibrations, a score changes only when the data changes.
The usual approach answers your first request with a project: someone labels data, then builds, tests, launches, and monitors a model. Intelafy answers it with a sentence, because the engine has already measured how the traits in the data relate.
Because every trait is measured the same way and placed on the same 0-to-1 scale, you can build an audience with simple math on the scores, and the same math works across more than 100,000 traits.
Each one comes from measuring instead of training.
The engine calculates how traits relate directly from the data, using fixed math instead of a trained model. Same data in, same answer out.
Every score sits on one 0-to-1 scale where 0.25 always means "no signal." Scores for truck buyers, cruise enthusiasts, and registered voters can be compared directly.
A large market-research survey covers only a sample of people. Intelafy extends its answers across everyone in a national file, so the attitudes and interests the survey asked about can be scored for each person, with no models to train.
Off-limits data is removed before scoring, so the engine can't see it. An auditor gets proof, not a promise.
Between scheduled recalibrations, a score changes only when the data changes, so you can tell whether the campaign worked or the audience changed.
One file that is the marketing list, the media plan, and the creative brief at once, with scores for each person's behavior, motivation, best channel, and creative angle.
Market-research surveys are expensive and cover only a small sample. The bridge extends a large survey's answers across a national file of hundreds of millions of people, which comes to trillions of predictions from one engine, with no models to train for each trait.
An analyst typed a seven-word request into the workspace. About an hour later, the session had produced a scored national audience, motivation segments, channel and creative plans, and a five-tier map of a multibillion-dollar category. The scores share one measured scale, and nobody wrote code or trained a model. Illustrative example; figures anonymized.
The definition centers on how recently people bought. The engine sizes the audience (28.4% of the file) and files it.
Everyday Family Buyers 14.2% · Performance Loyalists 6.5% · Hype Buyers 3.8% · Athleisure & Comfort 3.5%.
Delivery anchors and content cues are appended on the same 0-to-1 scale, which puts the media plan and creative brief inside the file.
From the biggest global brands down to niche players, each mapped to a segment, including which competitors' customers to go after.
Intelafy is built to measure audiences from organized consumer data, where being able to explain, compare, and stand behind a score is what matters. It is not built for reading raw text, images, or audio; predicting your next click in real time; or powering giant recommendation feeds.
Book a Data Discovery on your data. We'd rather be tested on your file than on our argument.
