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Clean-room activation.
Match and model inside the publisher's clean room. The seed file never leaves its perimeter.
Where it runs
The data stays live, governed, and yours.
Your CRM, your loyalty file, your behavioral signals, activated across every media channel, never copied, never resold.
First-party data is the most defensible signal a brand owns. Customers who bought, lapsed, opened, returned, complained, advocated, every event is a targetable, modelable signal that doesn't depend on a third-party cookie.
The risk is in the activation. Most "data activation" workflows export the seed audience into a third-party graph, where it leaks, gets resold, or becomes a tax against the next campaign. Clean activation runs inside a clean room: the data is matched, modeled and deployed without ever leaving its perimeter.
Named
Direct supply
Named publishers are the default path.
Pre‑bid
Fraud screening
Screening built to catch invalid traffic before budget moves.
Early
First reads
Domain-level visibility soon after launch.
Traceable
Claim to source
The numbers we publish tie back to named, dated sources.
Match and model inside the publisher's clean room. The seed file never leaves its perimeter.
Hashed-email + phone + mobile-ID waterfall. ~70-80% match rate on most CRM files.
Activation runs against modeled lookalikes, not raw IDs. Privacy-preserving by construction.
Existing-customer suppression, lapsed-customer reactivation, complaint-list exclusion, applied at bid.
Real units from our showcase, running live. Hover any one to play it.
The data stays live, governed, and yours.
First-party activation is the front door. Behind it: three more disciplines and one proprietary signal source — same clean-room rules, same named sources, one team.
“Identity isn’t a vendor pick. It’s a waterfall.”
The identity layer is the substrate every audience play depends on: frequency caps, first-party activation, attribution. We run it as a waterfall — deterministic IDs where available, household graph where they fail, privacy-safe cohorts where neither holds — with one household definition applied across CTV, display, social and retail media.
“A lookalike is only as good as the seed it grew from.”
Lookalike modeling extends a known seed — buyers, high-value customers, lapsed-but-recoverable — toward prospects who share the seed’s behavioral signal. The quality lives in three layers: the seed, the model, and the activation. We audit the seed before modeling, document what drives the expansion, and cap its depth at the point returns start to dilute.
“Context is the cookieless future’s first answer.”
Contextual was the original programmatic signal and is becoming the most defensible one. Modern contextual is AI-classified semantic intent — topic, sentiment, reader intent and brand-safety attributes, classified at bid time rather than pulled from a stale keyword taxonomy — with custom intent classes built per brand.
“Real people, telling each other what to buy.”
AdForge listens to 30 live sources of real consumer voice — TikTok comments, Amazon reviews, Reddit threads — screened against paid-disclosure patterns before anything counts. That corpus is ours alone, and it doesn’t stop at briefs: the behaviors behind the verbatims become modelable seeds, activated programmatically across the same channels as everything above.
Tell us what you’re running. We’ll show you where it’s actually running, and what that’s worth.