PulseToPurchase interface Device

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

Recommendation System
"Others Also Bought" Static Rules

Based on historical purchase data without considering current behavior

Typical Performance
3.2%

Recommendation click-through rate

2.1%

Conversion from recommendations

+8%

Average order value increase

Key Problem: Recommendations don't align with customer's current shopping intent

After: PulseToPurchase AI

Recommendation System
Real-time Intention Analysis

Adapts dynamically based on browsing patterns and micro-intentions

PulseToPurchase Results
27.8%

Recommendation click-through rate

12.5%

Conversion from recommendations

+18%

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.

PulseToPurchase solution
1

Self-learning Algorithm

With each session, our system gets smarter, recognizing more and more behavior patterns.

2

Dynamic Adaptation

Recommendations change in real-time – responding to every click and every second of the session.

3

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):

+27%

Recommendation click-through rate

+12%

Conversion rate

+18%

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