A brain for your storeintent that updates as they browse.
Anthos reads every meaningful signal—paths, pauses, search, cart edits—and builds a live model of what shoppers want. Your storefront reacts in real time: layout, copy, assist, and offers tuned to the moment.
Intent recomputed on every meaningful signal
Landed · /sale
Deal-oriented entry
PDP · running shoes · size hover
Sizing anxiety · performance runner
Search · “wide fit”
Constraint clarified → narrow assortment
Cart · add + remove liner
Hesitation on accessories
Now showing
Wide-fit runners + fit guide above the fold
- Hero → social proof on fit
- Assist → sizing FAQ
- Promo held until cart steady
01 — The gap
Clicks are loud. Intent is quiet—until you listen.
Most storefronts optimize for averages: same hero, same modules, same journey. Shoppers signal what they need through how they move—but stacks rarely close the loop in time to change the experience.
What stores see today
- Page views & funnel steps
- Segments that update nightly
- Rules that fray at the edge cases
What Anthos adds
- Streaming behavioral context
- Intent that tightens as they browse
- Hooks to change UI in the same session
02 — Intent layer
A live read on what this shopper is trying to do—right now.
Anthos ingests navigation, dwell, search refinements, PDP interactions, and cart mutations. It fuses them into a structured intent state your frontend and automations can query—updated continuously, not batched into yesterday's segment.
Signal ingestion
First-party events from your storefront—privacy-preserving, no sketchy third-party graph.
Intent graph
Goals, constraints, and confidence scores that evolve as the session deepens.
API for the moment
One call: “what should we show?”—aligned to merchandising rules you control.
03 — React
Dynamic storefronts—not another static A/B matrix.
When intent shifts, your experience should too. Anthos outputs decisions your UI understands—which modules to emphasize, what copy angle to lead with, when to surface assist, and how aggressive promotions should be.
Example adaptations
- Reorder blocks on the homepage based on goal (gift vs. replenish vs. explore).
- Tune collection ranking when search + dwell show narrowing interest.
- Open contextual assist with the right FAQ—not a generic chatbot.
- Hold or release promos based on hesitation signals in cart.
{
"goal": "find_wide_fit_runner",
"confidence": 0.86,
"ui": {
"hero": "fit_social_proof",
"assist": "sizing_faq",
"promo": "hold"
}
}04 — Platform
Plugs into the stack you already run.
The point isn't more dashboards—it's a closed loop between behavior and the very next screen they see.
Ship a store that listens
We're onboarding commerce teams building the next layer of personalization—intent-native, real-time, and merchant-controlled.
shettynirek@gmail.com