Questions about money anxiety rose 38% in September
Back-to-school costs pushed budgeting and “how do I save” questions to their highest level this year.
Full article coming soonEngine
The intent model behind everything we build: how it works, where its data comes from, and what it's teaching us.
Part 1
Camino isn't a single model or a single score. It's a four-stage pipeline that turns raw, fragmented signal into a structured understanding of intent, and keeps that understanding current as people keep interacting.
Track each user’s conversational, behavioral, and contextual signals in real time.
Embed each user’s signals into a continuously evolving intent graph.
Low-latency inference serves the graph where it's needed.
Every activation is a new signal that feeds back in.
Capture. Every surface we touch, whether it’s a conversational turn inside a Camino Apps experience, a scroll or dwell event, a piece of content itself, or a signal an advertiser already holds, is recorded with its source, timing, and context intact. This forms the engine’s evidence layer: what users explicitly share, how they behave, and the content and context surrounding their interactions.
Model. Transformer embeddings map signals from user conversations, content, and behavior into a shared intent space. Rather than collapsing a person into a single propensity score, we build a persistent, per-user intent graph: a structured, evolving map of what someone cares about, how strongly, and why.
The graph evolves continuously, keeping pace with each user’s changing interests, needs, and intent.
Activate. A low-latency inference layer serves that graph wherever it's needed - shaping a response inside a Camino Apps conversation in milliseconds, riding along as a bid-time signal when publishers enrich their bidstream, or landing as structured audience data inside a publisher's CRM and Google's Publisher Provided Signals (PPS) when they understand their audience.
Learn. Every activation is itself a new signal. Outcomes feed back into the graph, so the engine recalibrates continuously - it gets more accurate the more it's used, not just the more it's retrained.
Signal capture is consent-based. The graph is built from what people tell us and do across Camino's own apps and partner publisher pages.
The same intent graph powers a publishing experience, a bidstream integration and a CRM export. One representation of intent, activated through different APIs.
Inference is built to run inside the ad call and inside the conversation - not as an offline enrichment step that arrives too late to matter.
Outputs are structured and interpretable. Partners see the intent signals behind a decision, not just a number they have to trust.
Part 2 · Our data
Two first-party sources, both consented: Camino's own conversational apps, and the partner publishers that run Camino for Publishers.
Camino Apps
Astrology Daily, My Daily Bible and Thrillly are conversation-first apps used by 20M+ registered people, with 1.5B+ interactions so far. Those conversations are the Engine's core signal.
Partner publishers
Publishers running Camino for Publishers add signal from their readers' conversations and reading behavior, across a growing network of sites in multiple verticals and markets.
Broader than any single app
Camino's signals span the topics people bring to conversation every day: faith and spirituality, astrology and wellbeing, relationships, entertainment and more, across our own apps and our partner publishers' content.
Built on consent
Part 3
Conversational intent data is information about what people want, taken from the questions they ask and the topics they explore in AI conversations rather than inferred from clicks or cookies. Camino collects it with consent in its own apps and on partner publisher pages.
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Full article coming soon