Recommendation Engines
Stratnova Tech

Recommendation Engines

Our engineers design real-time recommendation systems using TensorFlow, PyTorch, and collaborative filtering algorithms. We implement content-based and hybrid models optimized with user behavior data and product affinities for personalization at scale.

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Custom-Built Recommendation Systems for E-commerce, Media, and Enterprise Applications

In the modern digital economy, personalized recommendations are no longer optional—they’re essential for driving revenue and improving customer satisfaction. At Stratnova, we specialize in building AI-powered recommendation engines that learn from user behavior, predict preferences, and deliver relevant suggestions at precisely the right moment.
Our recommendation solutions leverage machine learning algorithms, collaborative filtering, deep learning, and content-based modeling to ensure your platform delivers an engaging, tailored experience. Whether you need a product recommendation engine for an e-commerce site, content personalization for a media platform, or custom AI-powered matching algorithms for enterprise workflows, we build systems that are scalable, secure, and highly accurate. By integrating seamlessly with your existing tech stack—whether it’s a web application, mobile app, or SaaS platform—our recommendation engines operate in real-time, constantly refining their predictions to adapt to evolving user preferences and behaviors.

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Recommendation Engines
OUR CLIENTS

Trusted by Industry Leaders

From startups to Fortune 500 companies, leading businesses trust us with their digital transformation

Our Expertise in Recommendation Engine Development

We combine state-of-the-art algorithms with enterprise-grade engineering to deliver fast, accurate, and scalable personalization systems.

Collaborative Filtering Systems

Predict user preferences by analyzing behavioral patterns and identifying similar users.

Content-Based Filtering

Match users with relevant products or content based on item attributes and user history.

Hybrid Recommendation Models

Combine collaborative and content-based techniques for higher accuracy and diversity of results.

Context-Aware Recommendations

Personalize suggestions using contextual data like time, location, and device type.

Real-Time Personalization

Stream recommendations instantly using streaming data pipelines and low-latency inference.

A/B Testing and Optimization

Continuously evaluate and fine-tune algorithms for maximum conversion impact.

10+

Empowered Clients

Clients who trust us with their digital transformation.

10+

Countries Served

Global reach across multiple countries and regions.

10+

Full-time Tech Team

Expert developers, designers, and marketing professionals.

10+

Years of Experience

Years of expertise in cutting-edge technology.

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Our Offerings

Discover our specialized service offerings designed to drive innovation and deliver exceptional results for your business

Our Offerings

E-commerce Product Recommendations

Increase sales with AI-driven product suggestions, upsells, and cross-sells tailored to each shopper.

Content Personalization Engines

Deliver relevant articles, videos, or music based on user preferences and consumption patterns.

Enterprise Workflow Matching

Optimize internal processes by intelligently matching resources, tasks, or clients based on historical data.

Our Development Process

From startups to Fortune 500 companies, we've delivered exceptional results that drive growth, innovation, and success across diverse industries.

9:41
SN

Statnova

Productivity Suite

Today's Focus

"Build, Test, Launch!"

Define Requirements
Design UI/UX
Develop Features
Test & QA
Launch & Support
Progress 20%
v2.1.0 statnova.com

Planning

We begin by understanding your business objectives, target audience, and project requirements. This thorough discovery phase allows us to create a detailed roadmap and strategy tailored to your specific needs and goals.

Designing

Our design team creates intuitive user experiences and visually appealing interfaces that align with your brand identity. We focus on creating user-centered designs that drive engagement and conversions while ensuring accessibility.

Development

Our skilled development team transforms designs into fully functional products using modern technologies and best practices. We employ agile methodologies to ensure flexibility, transparency, and continuous delivery throughout the process.

OUR TECH CAPABILITIES

Built on the Backbone of Modern Tech

Scalable, secure, and future-ready solutions built on the latest tech stack.

Core Features of Our Recommendation Engine Solutions

Our recommendation systems are engineered for accuracy, speed, and measurable business growth.

Multi-Algorithm Approach

We use collaborative filtering, matrix factorization, deep neural networks, and reinforcement learning to deliver the most relevant results.

Scalable Cloud Infrastructure

Our solutions are deployed on AWS, Azure, or GCP, enabling them to handle millions of recommendations per second.

Continuous Learning Pipelines

Automated retraining ensures your recommendations stay relevant as trends and user preferences evolve.

Transform Engagement with Intelligent Recommendations

Whether you’re selling products, delivering content, or matching services, our AI-powered recommendation engines will help you deliver the right experience at the right time.

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CLIENT TESTIMONIALS

Why businesses choose Stratnova

Real results from real clients across industries

"Stratnova brought our vision to life with an enterprise-grade CRM that has transformed how we manage sales and customer relationships."

Stratnova turned our vision into reality with an enterprise-grade CRM that redefined how we manage sales and customer relationships. Their clean, scalable code, rapid delivery, and proactive communication made the process seamless. It truly felt like we had an in-house tech team dedicated to our success.

Client Photo
Andrés Romero
Chief Technology Officer
Company Logo
Project
Custom CRM Software for a B2B Energy Supplier
Spain
Increased deal conversion by 37% within 6 months.
50K+ Active Users

"We approached Stratnova with just an idea for a fitness app — and they delivered a beautifully designed, fully functional cross-platform app that users love."

Starting with just a concept for a fitness app, Stratnova transformed our vision into a sleek, fully functional cross-platform product that resonates with users. From initial design sketches to final launch, their approach was seamless, transparent, and highly collaborative.

Client Photo
Rachel Tan
Founder & CEO
Company Logo
Project
Health & Fitness
UAE
4.8/5 Rating on Play Store
iOS & Android

"Stratnova completely revamped our digital marketing strategy and helped us scale lead generation by 4x in under 3 months."

In just three months, Stratnova’s overhaul of our digital marketing efforts skyrocketed lead generation by 4x. Their sharp focus on performance—through expertly managed paid ads, targeted SEO, and a US-market-specific content strategy—delivered measurable, high-impact results.

Client Photo
Mark Johnson
Marketing Director
Company Logo
Project
Technology
United States
4x Increase in Leads in 90 Days
Performance-Driven ROI

Frequently Asked Questions

Get answers to common questions about our services and solutions.

What types of recommendation engines do you develop?

We build collaborative filtering, content-based, hybrid, and context-aware recommendation engines depending on the project requirements and data availability.

How do recommendation engines improve conversions?

By delivering highly relevant suggestions, recommendation systems increase click-through rates, order values, and repeat purchases while reducing churn.

Can you integrate recommendation engines into existing platforms?

Yes—we design API-driven solutions that integrate seamlessly with e-commerce platforms, CMSs, mobile apps, and SaaS products.

What technologies do you use for recommendation engines?

We work with Python, TensorFlow, PyTorch, Apache Spark, Elasticsearch, and cloud ML services like AWS Personalize and Google Recommendations AI.

How do you ensure recommendations are accurate?

We apply continuous evaluation with metrics like precision, recall, MAP (Mean Average Precision), and CTR (Click-Through Rate), and regularly retrain models.

Do you provide real-time recommendations?

Absolutely—we build streaming architectures with low-latency inference for instant personalization.

Can your recommendation engines handle large-scale data?

Yes—our cloud-native designs can process millions of records and handle heavy concurrent traffic.

How do you address cold start problems?

We implement hybrid models, user onboarding surveys, and content-based techniques to provide recommendations even with minimal user data.

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Ready to transform your business with innovative technology solutions

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Ready to transform your business with cutting-edge technology? Let's discuss your project and create innovative solutions together.

What happens next?

1

A Stratnova expert contacts you after analysing your requirements

2

If needed, we sign an NDA to ensure the highest privacy level

3

We submit a comprehensive project proposal with estimates, timelines, CVs, etc.

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