Build AI-based recommendation systems that deliver relevant suggestions across your products. Our team designs and implements scalable recommendation solutions that support business goals and integrate seamlessly into existing platforms.
Amazon SageMaker
Microsoft Azure ML
Google Vertex AI
Featured partners
We provide end-to-end AI recommendation system development, from strategy and data analysis to seamless integration and ongoing optimization
Evaluating your business goals and data sources to craft a tailored strategy that aligns with your needs and platform capabilities
Structuring and preprocessing your data, ensuring it’s clean and ready to power accurate, personalized recommendations
Creating tailored AI-driven recommendation engines that seamlessly fit into your platform, boosting user engagement and conversions
Integrating recommendation engines into your systems and continually fine-tuning them to ensure ongoing accuracy and relevance
By integrating AI recommendation systems into your software, you can unlock new growth opportunities
71%
71% of consumers now expect personalized recommendations. At the same time, 76% feel disappointed when they don’t receive tailored suggestions
150%
Businesses that leverage AI-powered recommendation engines experience a 150% increase in CTR, leading to higher engagement and boosting sales
40%
Sites that invest in personalized experiences generate 40% higher revenue than those that don’t, as personalization helps engage customers
AI-based recommendation solutions bring value for various business domains, enhancing customer engagement and satisfaction
Our AI engineers design various types of AI recommendation engines, considering your business model and software specifics
We empower solutions with AI-based recommendation systems that help businesses deliver personalized experiences by analyzing user preferences and behavior
We built a web platform for finding activities and accommodations, integrating AI-based recommendation systems that personalize suggestions by analyzing user preferences and ranking results for better relevance.
We developed a B2C marketplace platform that connects users with service providers. The platform features a custom AI-based recommendation system that tailors product suggestions to user preferences and browsing behavior.
Learn about Cleveroad’s expertisein Projects Portfolio
in Projects Portfolio
Show moreWe keep deepening our expertise to meet your highest expectations and build business innovative products
ISO 27001
Information Security Management System
ISO 9001
Quality Management Systems
AWS
Select Partner Tier
AWS
Solutions Architect, Associate
Scrum Alliance
Advanced Certified Scrum Product Owner
AWS
SysOps Administrator, Associate
USACTO at Proprio Cloud Solutions
"Sprint velocity went up 30 to 40 percent with Cleveroad's AI-assisted team, without growing headcount or dropping code quality."
Our team of AI engineers designs and integrates personalized recommendation systems that enhance user experiences and drive business value
We begin by understanding your business goals and user data to define the best approach for your AI recommendation system. Our team works together to outline key use cases, shape the project vision, and create a clear roadmap for development.
We build a Proof of Concept (PoC) to validate your core idea. The team selects and fine-tunes the most suitable models, ensuring the system performs optimally before scaling to additional use cases and optimizing for real-world scenarios.
Once the PoC is validated, we integrate the recommendation engine into your platform. Our focus is on seamless integration with your existing tools and interfaces to ensure stable and smooth operation of the recommendation system.
After launch, the system undergoes continuous optimization based on real user feedback. We focus on improving recommendation accuracy, fine-tuning algorithms, and ensuring scalability to meet growing demands and business goals.
We begin by understanding your business goals and user data to define the best approach for your AI recommendation system. Our team works together to outline key use cases, shape the project vision, and create a clear roadmap for development.
We build a Proof of Concept (PoC) to validate your core idea. The team selects and fine-tunes the most suitable models, ensuring the system performs optimally before scaling to additional use cases and optimizing for real-world scenarios.
Once the PoC is validated, we integrate the recommendation engine into your platform. Our focus is on seamless integration with your existing tools and interfaces to ensure stable and smooth operation of the recommendation system.
After launch, the system undergoes continuous optimization based on real user feedback. We focus on improving recommendation accuracy, fine-tuning algorithms, and ensuring scalability to meet growing demands and business goals.
To deliver accurate, low-latency recommendations, we combine production-grade data, ML, and serving tools that scale reliably
AWS SageMaker
Azure Machine Learning
OpenAI
Google Vertex AI
Hugging Face Hub
XGBoost
LightGBM
PyTorch
TensorFlow
TensorFlow Recommenders
Elasticsearch
OpenSearch
Apache Kafka
Apache Spark
Redis
Kubernetes
MLflow
Prometheus
Grafana
Datadog
We help companies design and implement recommendation engines that improve user experience and support measurable business outcomes
Our team has practical experience building AI-based recommendation systems for marketplaces and media-driven platforms, where personalization accuracy directly impacts user engagement. We design systems that handle real-world data and scale reliably as usage grows.
We integrate recommendation engines into your existing backend and analytics infrastructure. Our AI components align with current data flows and system architecture, reducing integration friction and avoiding disruption to established workflows.
Our experience across Travel, Marketplaces, Media, Retail, and FinTech enables us to adapt recommendation logic to different use cases rather than applying generic models, so our recommendation systems reflect how users make decisions within a specific product context.
We offer flexible cooperation models, from staff augmentation and dedicated teams to full-cycle custom development. This approach allows you to scale AI expertise alongside product growth, control costs, and integrate our engineers into your workflows with minimal overhead.
70 clutch reviews
4.9
Award
Clutch 1000 Service Providers, 2024 Global
Award
Clutch Spring Award, 2025 Global
Ranking
Top AI Company,
2025 Award
Ranking
Top Software Developers, 2025 Award
Ranking
Top Web Developers, 2025 Award
Ranking
Top Staff Augmentation Company in USA, 2025 Award
