Realized projects
Rated by 44 clients
Years of engineering excellence and tech innovation
Design and train machine learning recommendation models, integrate them into any platform, and optimize performance to ensure accuracy and relevance, maximizing customer satisfaction and revenue
We deliver tailored AI-driven solutions that enhance user engagement and personalization. Our process includes selecting the right algorithms, developing scalable data pipelines, and integrating real-time recommendation features
We provide strategic guidance on selecting the right algorithms, integrating AI seamlessly into existing platforms, and optimizing models for peak accuracy
Seamlessly implement AI-driven recommendation engines into any platform to deliver personalized product suggestions, content recommendations, and customer insights. IoT devices, CRM systems, analytics, marketing systems, etc.
Our recommendation system maintenance service keeps AI-driven recommendation engines accurate, up-to-date, and continuously evolving. We provide ongoing monitoring, performance tuning, and model retraining
Fine-tune and upgrade existing AI-driven recommendation engines. We analyze system performance, identify inefficiencies, and refine algorithms to better adapt to evolving customer behaviors
Develop advanced content recommendation systems that suggest articles, videos, and products. Our solutions leverage diverse data sources, including text, ratings, and user behavior, to implement content-based and collaborative filtering for highly relevant recommendations
Hybrid recommendation engines combine multiple algorithms to deliver highly accurate and tailored suggestions. By leveraging diverse data sources, this approach integrates content-based and collaborative filtering, enhancing customer satisfaction and driving revenue growth
Suggest related, complementary, and alternative products to boost online revenue and increase average order values. Our solutions also deliver after-sales recommendations, enhancing customer engagement and retention
User-centric recommendation engines prioritize individual needs, lifestyles, and expertise to deliver highly relevant content or product suggestions. These systems incorporate expert insights and product attributes to refine recommendations, ensuring a more personalized and precise user experience
Develop advanced visual search for recommendation engines, search platforms, and product catalogs. Using deep learning, we train image recognition models to accurately identify unique images and scan vast databases to find visually similar objects, delivering seamless and intuitive search functionality
AI-based recommendation systems analyze user interactions to deliver personalized suggestions. By extracting key features from content, our approach computes similarities between items, ensuring users receive the most relevant recommendations
2/3
movies are being watched upon the AI-based recommendation
38%
more clicks driven by recommended content
35%
sales result from Al recommendations
Deliver unique, AI-driven product recommendations tailored to each customer for a truly personalized experience
Provide customers with personalized bundle discounts, tailored to their interests and shopping behavior for a more engaging and rewarding shopping experience
Ditch irrelevant offers — deliver the right products to the right customers at the right time for smarter, more effective engagement
Identify target audiences based on real needs, not just formal characteristics. Group customers into dynamic clusters using implicit connections between attributes
Analyze customer interactions on a website or app to assess their attitudes toward products or services. Get comprehensive analytics for every recommendation, ensuring smarter, data-driven decision-making
Keep users engaged with relevant content, reducing drop-off rates and increasing customer lifetime value
We specialize in the development of customized AI recommendation systems, tailored to meet the specific needs of each business. Our team conducts a thorough analysis of individual requirements to deliver solutions that effectively address key challenges and enhance decision-making
Fully tailored to business needs, customer behavior, and unique data patterns
Limited customization; generic algorithms for broad use cases
Scales with business growth, handling increasing data volumes and complexity
Predefined capacity, may struggle with scaling efficiently
Seamless integration with existing infrastructure and third-party tools
May require extensive workarounds for compatibility
Uses advanced, business-specific algorithms for superior accuracy
Uses standard algorithms with limited tuning options
Optimized for proprietary data, leveraging first-party insights
Often relies on pre-built datasets, limiting adaptability
Unique recommendation strategies can differentiate the business
Competitors may use the same system, reducing uniqueness
Fine-tuned to deliver high accuracy based on real-time data
Generalized models may not optimize for specific needs
Higher initial investment but lower long-term costs due to optimization
Lower upfront cost but potential high recurring fees
Renata Sarvary
Business Development Manager
Plavno has a team of skilled developers ready to tackle the project. Ask me!
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Looking to boost your business profits with personalized product recommendations? Get in touch with us today!
Agile teams are primed for deployment within 2-3 weeks.
Well-defined service standards for all clients.
of developers are senior-level.
The Al assistant stores and tracks all candidate interactions. Once a user submits their information, they are identified within the system, allowing the Al to review their application history and determine the next steps. It also conducts background checks using open sources to flag any criminal records or administrative violations. If a candidate passes these evaluations, the Al schedules an in-person interview at a company office
Results34%
Less in hiring costs
SecureLi’s Al assistant has revolutionized its customer support operations by handling 100% of incoming calls, efficiently managing the first level of support. The assistant successfully automates 57% of these calls, or 63% when factoring in staff time. On average, employees now spend only 2-3 seconds to take a call and an additional 15-20 seconds to update customer records after the conversation
Results63%
Resolved issues without forwarding to a consultant
In close collaboration with the client we developed and introduced a guided tutorial tour that highlights key features and actions within the platform, ensuring new users are familiar with how to navigate, select quests, and interact with AI chats
Results51%
Increase in user satisfaction
You schedule a meeting with our product development team at a time that works for you
You share your product idea and ask any questions you have after we sign an NDA
We outline how we can assist you in developing and bringing your product idea to life
By facilitating individualized treatment plans and optimizing medication recommendations based on patient history, recommendation systems have made significant advancements in both medical research and patient care
We develop custom recommendation systems for eCommerce companies with the goal of raising revenue and enhancing customer satisfaction. Our solutions provide highly customized product recommendations by examining past purchases, current trends, and customer behavior
We develop AI-driven recommendation engines that enhance financial decision-making in banking, investments, savings, and insurance. Powered by machine learning, our solutions provide personalized financial product recommendations, detect fraud, and optimize risk management strategies, ensuring smarter and more secure financial choices
Plavno delivers an AI-powered recommendation engine designed to optimize production, streamline inventory management, and enhance procurement strategies. By leveraging data-driven insights, manufacturing companies can reduce costs, improve operational efficiency, and make smarter, more informed decisions
Plavno collaborates with leading hospitality brands to strengthen customer relationships through tailored digital solutions. We develop AI-driven systems that recommend personalized vacation packages, hotel stays, and adventure experiences based on travelers' preferences and past bookings, enhancing engagement and satisfaction
Entertainment platforms all over the world are embracing custom AI recommendation engine solutions. We create intelligent systems that examine user behavior and recommend highly relevant content in the media, gaming, and music streaming industries. This strategy increases audience engagement, increases retention rates, and maintains user immersion over time
By facilitating individualized treatment plans and optimizing medication recommendations based on patient history, recommendation systems have made significant advancements in both medical research and patient care
We develop custom recommendation systems for eCommerce companies with the goal of raising revenue and enhancing customer satisfaction. Our solutions provide highly customized product recommendations by examining past purchases, current trends, and customer behavior
We develop AI-driven recommendation engines that enhance financial decision-making in banking, investments, savings, and insurance. Powered by machine learning, our solutions provide personalized financial product recommendations, detect fraud, and optimize risk management strategies, ensuring smarter and more secure financial choices
Plavno delivers an AI-powered recommendation engine designed to optimize production, streamline inventory management, and enhance procurement strategies. By leveraging data-driven insights, manufacturing companies can reduce costs, improve operational efficiency, and make smarter, more informed decisions
Plavno collaborates with leading hospitality brands to strengthen customer relationships through tailored digital solutions. We develop AI-driven systems that recommend personalized vacation packages, hotel stays, and adventure experiences based on travelers' preferences and past bookings, enhancing engagement and satisfaction
Entertainment platforms all over the world are embracing custom AI recommendation engine solutions. We create intelligent systems that examine user behavior and recommend highly relevant content in the media, gaming, and music streaming industries. This strategy increases audience engagement, increases retention rates, and maintains user immersion over time
With a team of top developers in every field required for robust custom software development, Plavno has evolved into a major player in the global software engineering competition
"When purchasing a printer, don't forget to also pick up a compatible cartridge"
"You like action movies with Clint Eastwood, you might also like "The Good, the Bad, the Evil" movie"
"Diaper buyers frequently purchase baby powder alongside their diapers. Do you need baby powder as well?"
Training in support vector methods, linear discriminant analysis, and singular value decomposition for implicit functions
Recommendations based on human social interactions
Combination of any methods and approaches
"When purchasing a printer, don't forget to also pick up a compatible cartridge"
"You like action movies with Clint Eastwood, you might also like "The Good, the Bad, the Evil" movie"
"Diaper buyers frequently purchase baby powder alongside their diapers. Do you need baby powder as well?"
Training in support vector methods, linear discriminant analysis, and singular value decomposition for implicit functions
Recommendations based on human social interactions
Combination of any methods and approaches
Selecting the right engagement model depends primarily on project requirements and the complexity of the software development services
AI powers recommendation systems by analyzing user behavior, preferences, and interactions to deliver personalized suggestions. Using machine learning and deep learning, these systems identify patterns, refine recommendations, and enhance engagement across e-commerce, banking, and other industries.
Building an AI recommendation system starts with collecting and processing user interaction data, preferences, and item attributes. The next step is selecting a suitable model, such as collaborative filtering, content-based filtering, or deep learning techniques. Once the model is trained on historical data and optimized for accuracy, it is deployed and integrated into the platform. Continuous monitoring and updates ensure the system remains effective, adapting to user behavior and improving recommendations over time.
AI agents in recommendation systems act as digital curators, analyzing user behavior, preferences, and real-time interactions to deliver highly personalized suggestions. Powered by machine learning and deep learning, these intelligent systems continuously adapt, learning from every click, purchase, or view to refine their recommendations. Whether guiding shoppers to the perfect product, helping users discover new content, or tailoring service suggestions, AI agents enhance engagement and streamline decision-making. By transforming vast amounts of data into meaningful insights, they create a seamless and intuitive user experience that keeps audiences coming back for more.
The cost of developing a recommendation system depends on several factors, including the complexity of the model, the volume of data processed, the level of personalization required, and the integration with existing systems. A basic solution utilizing standard machine learning techniques may range from $5,000 to $50,000, while more advanced AI-driven systems incorporating deep learning, real-time processing, and dynamic personalization can cost between $15,000 and $100,000 or more. Additional costs may include infrastructure, data engineering, and ongoing system maintenance. The overall investment is determined by the specific business requirements, scalability needs, and desired level of customization.
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The 3D configurator has received positive feedback from customers. Moreover, it has generated 30% more business and increased leads significantly, giving the client confidence for the future. Overall, Plavno has led the project seamlessly. Customers can expect a responsible, well-organized partner.
Sergio Artimenia
Commercial Director, RNDpoint
Plavno has developed a web application for a product development company. They’ve built a 3D configurator that allows clients to design a unique final product by choosing colors, materials, and other options.
Read morePlavno's efforts in addressing challenges and implementing effective solutions have played a crucial role in the success of T-Rize. The outcomes achieved have exceeded expectations, revolutionizing the investment sector and ensuring universal access to financial opportunities
Thien Duy Tran
Product Manager
Review from Thien Duy Tran, T-Rize Group
Through the partnership with Plavno, we built a system used by more than 40 million connected channels. Throughout the engagement, the team was communicative and quick in responding to our concerns. Overall, we were highly satisfied with the results of collaboration.
Michael Bychenok
CEO, MediaCube
Plavno was hired by a YouTube network to develop an internal portal for bloggers that handled Google reports for their company. They also built an e-wallet and a system where users' information was collected.
Read morePlavno's codes and designs are user-friendly, and they complete all deliverables within the deadline. They are easy to work with and easily adapt to existing workflows, and the client values their professionalism and expertise. Overall, the team has delivered everything that was promised.
Helen Lonskaya
Head of Growth, Codabrasoft LLC
A mobile app development company has hired Plavno to create a secure digital system, combining health testing with advanced mobile technologies. They've also made the app functional for iOS and Android devices.
Read moreThe app has been designed to be user-friendly and intuitive, allowing users to easily search for flights, hotels, and car rentals, Plavno has an impressive and comprehensive understanding of our business model and value proposition, which they were able to quickly absorb.
Yana Romanovskaya
Project Manager
Plavno built a software development firm's travel app for iOS and Android. They designed the UI/UX, integrated payment gateways, and implemented registration, booking, translation, and other features.
Read moreThe MVP app developed by Plavno is excellent and has all the functionality required. Plavno has delivered on time and ensured a successful execution via regular updates and fast problem-solving. The client is so satisfied with Plavno's work that they'll work with them on developing the full app.
Mitya Smusin
Founder, 24hour.dev
Plavno has developed the MVP of a gig work history data collection app for a React frontend development company. Features include user registration, earnings data collection, data aggregation, and data graphs.
Read more
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In today's fast-paced world, embedded software development plays a crucial role in powering various devices that surround us. From smartphones to home appliances, automotive systems to medical equipment, embedded software is the invisible force that makes these devices function seamlessly.
We announce the latest feature at Plavno Software Development - the Product Cost Estimator! At Plavno, we are constantly striving to enhance our user experience and provide innovative solutions to meet the evolving needs of our clients. With the Product Cost Estimator, we are putting the power of informed decision-making in your hands.
Launching a successful startup or groundbreaking app isn't just about a brilliant idea; it's about the execution.
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