Why Syntrologie? It keeps learning to sell.
You're buying an intelligence team, not a plugin. Dozens of specialized systems read each shopper, deliberate, and compose one adaptive experience, and every sale teaches them something.
Intelligence driving results
You're buying an intelligence team, not a plugin.
Dozens of specialized systems read each shopper, deliberate, and compose one adaptive experience. It's the reason this can't be built with a single prompt.
Built by operators who've shipped personalization at scale
Stage 1 · Signals
66+ live signals · every shopper
Everything Syntrologie observes.
Behavior
- Scroll depth
- Product comparisons
- Return visits
- Cart hesitation
+12 more behavior signals
Catalog
- Live inventory
- Fabric & material
- Fit & sizing
- Bundles
+8 more catalog signals
Proof
- Reviews that convert
- Sentiment
- Persona match
- UGC & Q&A
+6 more proof signals
Identity
- Purchase history
- Lifetime value
- Size profile
- Past returns
+5 more identity signals
Context
- Device
- Geo
- Time of day
- Referral source
+7 more context signals
Outcomes
- Add-to-cart
- Purchase
- Returns
- What converted
+4 more outcomes signals
Signals feed the specialists
Stage 2 · Specialist models
30+ specialized models
An orchestra of specialists, in concert.
Intent Classifier
Infers the goal behind the session's behavior.
Archetype Model
Places the shopper among learned behavioral cohorts.
Churn-Risk Model
Flags the shoppers about to disengage or abandon.
Bundle & Cross-Sell
Finds the complements this shopper has shown intent for.
Next-Best-Action
Chooses the single highest-expected-value move.
Copy Recommendation
Selects the message and framing that lands for this shopper.
Affinity Ranker
Ranks products by fit to this shopper, not the crowd.
Review Resonance
Scores which reviews will actually convert this shopper.
Article-Intent Affinity
Matches editorial and guides to what the shopper wants to learn.
+21 additional specialists
Specialists drive results
Stage 3 · Drives results
Modeled outcomes
The intelligence shows up
where it counts.
Illustrative outcomes · modeled on comparable personalization deployments
A system that keeps learning.
Every change is measured against your current store. Every visit teaches it something, every way of selling is tested, and the winner keeps selling across your store and every AI channel.
Each shopper shows it what they came for and what moved them to buy.
Ways of selling compete for real, and the numbers decide.
What works for one shopper becomes how it sells to the next.
What sells best this week
Illustrative experiment · add-to-bag rate
See it sell on a store like yours.
The products