If you're an insurer reaching people likely to switch plans — under health-privacy and state privacy laws — Intelafy leaves regulated health data out before scoring, then grows a proven starting list using measured evidence, not a black-box look-alike model. A starting list of several million likely switchers became millions of prospects, every one of them traceable.
A standard look-alike trains a model on a small starting list and over-fits it. Intelafy grows the list by evidence instead — measuring each new person against the pattern of the starting group, with the starting group's own defining details left out, so the matches rest on independent evidence.
Health conditions, treatments, and other protected details are left out before scoring — so the engine truly can't see them. State health-privacy rules are enforced by location at the moment of scoring. Privacy isn't a policy added on top; it's built into how the engine works.
An auditor gets proof, not a promise: the exact, named features behind every score — and a demonstration that the regulated data was never in the pool.
Regulated health data leaves the pool before scoring — nothing can rebuild it.
State privacy rules applied as scores are made — not left to a later filter.
Every score ships with the named evidence that produced it.
Growing the list by evidence beats over-fit look-alikes — and every prospect is traceable.
Scores that hold still; any lift is your offer and creative, never the tool wobbling.
Direct mail, digital, and streaming-TV likelihoods added on the same scale.
Intelafy's methods are designed to support health-privacy and state consumer-privacy compliance (including HIPAA where it applies). Whether any specific use is compliant depends on your data, your use, and your counsel.
Send us a starting list. We'll return a measured expansion with a full evidence record, privacy built in.
