Our algorithms analyze shopper behavior in real-time to deliver recommendations based on their actual intentions — not guesswork.
See how our AI transforms shopping experiences and boosts conversions
Based on historical purchase data without considering current behavior
Recommendation click-through rate
Conversion from recommendations
Average order value increase
Key Problem: Recommendations don't align with customer's current shopping intent
Adapts dynamically based on browsing patterns and micro-intentions
Recommendation click-through rate
Conversion from recommendations
Average order value increase
Key Benefit: Recommendations evolve with each click to match customer's real-time purchasing intentions
Most recommendation systems work with simple rules: 'bought X, show Y'. But shoppers don't think in rules. Purchasing decisions are a sequence of micro-intentions – and that's exactly what we analyze.
Traditional engines rely on predefined rules that can't keep up with changing intentions
Recommendations don't align with the customer's moment of purchase readiness
Generic recommendations fail to reflect real purchasing intentions
PulseToPurchase is an intelligent AI-powered recommendation engine. It learns from every shopping session and adapts content dynamically – without manual configuration, without static scenarios. It works in real-time. It personalizes. It increases sales.
With each session, our system gets smarter, recognizing more and more behavior patterns.
Recommendations change in real-time – responding to every click and every second of the session.
We don't require rule definitions or complex setup. The system works autonomously.
For a clothing store (avg. 30K monthly visits), our AI identified 5 key purchasing intentions. Based on user behavior, we tailored real-time recommendations. Results (based on predictive models):
Recommendation click-through rate
Conversion rate
Average cart value
See how our AI engine can increase your sales without changing your layout
View Sample Results