AI-Driven Product Recommendations: Surface the most relevant products in real-time to increase upsell, cross-sell, and overall customer satisfaction.
Intelligent Customer Segmentation: Automate customer profiling with AI, enabling precise targeting for marketing campaigns and personalized engagement.
Scalable & Multi-Platform Integration: Connect multiple data sources beyond a single e-commerce platform to ensure more accurate and holistic personalization.

Summary

Smarter Recommendations. Higher Conversions. Engaged Customers.

The Personalization and Recommendation Engine helps retailers optimize product discovery, customer engagement, and conversion rates through AI-powered recommendations. By analyzing vast product catalogs and customer preferences, this solution provides intelligent, real-time recommendations, targeted marketing insights, and conversational product discovery. Designed for scalability, it connects with multiple data sources and supports various retail and e-commerce ecosystems without cloud vendor lock-in, maximizing personalization potential across platforms.

The Main Problem

Customers Struggle to Find Relevant Products, Reducing Engagement & Sales

Retailers with large and diverse product catalogs face challenges in helping customers discover relevant products efficiently. Traditional recommendation methods lack accuracy, leading to missed upsell opportunities and lower engagement. Without AI-driven personalization, retailers struggle with ineffective customer segmentation, high churn rates, and limited cross-channel personalization, resulting in decreased conversions and revenue.

Customers struggle to find relevant products in large catalogs
Limited data integration restricts recommendation accuracy
Lack of personalized engagement leads to higher churn

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Pain Point #1

Lost Sales Due to Ineffective Product Discovery?

Customers browsing vast product catalogs often struggle to find what they need, leading to frustration and lost sales. AI-driven recommendations analyze customer preferences and behavior in real time, surfacing highly relevant products to improve product discovery and boost conversion rates.

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Pain Point #2

Struggling to Deliver Targeted Customer Engagement?

Manual customer segmentation is slow, ineffective, and lacks precision. AI-powered segmentation automates and refines customer profiling, enabling hyper-personalized marketing campaigns and product recommendations that drive repeat purchases and customer loyalty.

Pain Point #3

High Customer Churn Due to Generic Shopping Experiences?

Without personalization, customers feel disconnected and are less likely to engage with a brand long-term. AI-driven conversational interfaces and predictive engagement strategies help retain customers, reduce churn, and create a tailored shopping experience that fosters loyalty and increases lifetime value.

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Don't just hear it from us

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JP Grace
Chief Technology Officer
Endear

As our customer base grew, we ran into PostgreSQL vertical scalability limits and problems like CPU, memory and connection exhaustion. We were thrilled the solution gave us a drop-in PostgreSQL replacement with much more efficient reads and writes. The solution requires less CPUs to hit our throughput and latency goals, lowering our cost by 40-50% and preparing us for the next phase of customer growth.

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