A brand-private model that predicts what every customer will be worth, and the intelligence platform built on top of it.
Every signal a brand owns flows into one private model, and everything it activates flows back out. The same model powers four intelligence products, so each one is built on the data you already have rather than a separate integration.
How healthy is my signal for campaign optimization?
Which customer micro-segments represent growth opportunities?
Multi-touch attribution. Which campaigns drive incremental outcomes?
Media mix modeling. Where should the next incremental dollar go?
You inherit a head start for free. Your customer data never leaves your private model environment, but shared aggregated, anonymous weights help the starting point get smarter with each run.
First-party behavior, transactions and campaign results joined to third-party device, household and interest attributes through a confidence-graded waterfall match. Match confidence carries forward as edge weight, so weaker links contribute less.
Event sequences and graph relationships are encoded into customer embeddings by the network itself, against the prediction objectives, rather than assembled as hand-built features.
One shared encoder, four heads: conversion likelihood, lifetime value, category and offer affinity, incrementality. Warm started from indexed weights rather than random initialization.
The trained instance goes into a model registry, is evaluated champion against challenger, then promoted to live inference.
Live predictions drive campaigns, and observed results come back as new labels for the next refresh.
Weights, never records. No customer data, no identifiers, no customer-level embeddings leave the brand-private boundary.
The shared index is keyed by product segment against customer cluster, and the nearest matching checkpoint warm starts the next private instance. That is the compounding part: the cold start for brand N+1 is shorter than it was for brand N.
A machine learning model trained on one brand's own customer data, and only that brand's. It learns from event sequences and graph relationships rather than hand-built features, and it predicts what each customer will do and what they will be worth. One shared encoder serves four prediction heads: conversion likelihood, lifetime value, category and offer affinity, and incrementality.
A CDP stores and segments customer records: it is a database with an audience builder on top. Angler is a model. It predicts outcomes that have not happened yet and sends those predictions into the ad auction as signal. A CDP can tell you who bought last month. The Customer Foundation Model tells you who is worth acquiring next month, and feeds that to platform AI automatically. Most brands run Angler alongside a CDP rather than instead of one.
No. Each brand's model is a private, isolated instance, and customer data never crosses its boundary. The only thing that ever crosses is learned weights: no records, no identifiers, no customer-level embeddings. Angler is SOC 2 certified.
Your model does not start from random initialization. It warm starts from weights learned by other trained instances, selected from a shared index keyed by product segment against customer cluster. You inherit a head start without anyone's data being shared, which is why a new instance reaches useful accuracy faster than the one before it.
Four weeks to a first measurable result. We demonstrate lift within an A/B test or incrementality study, so the lift is attributable rather than inferred, and you see it before committing to anything.
See the Customer Foundation Model on your own data, and prove the lift in a risk-free pilot.
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