Increase eCommerce sales with AI that understands your customers
Our algorithms analyze shopper behavior in real-time to deliver recommendations based on their actual intentions — not guesswork.
Sample Results
See how our AI transforms shopping experiences and boosts conversions
AI in Action: Real-time Recommendation Example
Before: Traditional Approach
"Others Also Bought" Static Rules
Based on historical purchase data without considering current behavior
Typical Performance
Recommendation click-through rate
Conversion from recommendations
Average order value increase
Key Problem: Recommendations don't align with customer's current shopping intent
After: PulseToPurchase AI
Real-time Intention Analysis
Adapts dynamically based on browsing patterns and micro-intentions
PulseToPurchase Results
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
The Problem
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.
Static Rules
Traditional engines rely on predefined rules that can't keep up with changing intentions
Poor Timing
Recommendations don't align with the customer's moment of purchase readiness
Lack of Personalization
Generic recommendations fail to reflect real purchasing intentions
The Solution
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.
Self-learning Algorithm
With each session, our system gets smarter, recognizing more and more behavior patterns.
Dynamic Adaptation
Recommendations change in real-time – responding to every click and every second of the session.
Zero Configuration
We don't require rule definitions or complex setup. The system works autonomously.
Simulated Implementation
Case Study: Fashion Retailer
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
How It Works
The AI-Powered Journey
Customer
Browses
AI Analyzes
Behavior
Predicts
Intention
Presents
Recommendation
Customer
Purchases
Intelligent Recommendations = Higher Revenue
See how our AI engine can increase your sales without changing your layout
View Sample Results