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We choose software development frameworks that support high-stakes digital transformations and align with fast-growth enterprise needs – accelerating time-to-market, lowering run-cost, and enabling developers to build without friction.
At Radixweb, we work with web and app development frameworks that deliver hyper-scalable architectures, AI-native capabilities, and zero-trust security to support distributed teams and real-time operations, all the while minimizing technical debt.
We religiously evaluate frameworks against modern enterprise benchmarks, prioritizing long-term cost impact, talent availability, and ecosystem maturity. Only stacks that meet these standards with active innovation roadmaps make the cut.

Selected for their impact on speed, reliability, and ROI, these frameworks form the backbone of our product engineering solutions – from prototypes and UIs to data pipelines and production AI models.

Angular keeps our enterprise frontends organized and powerful. We use it for big projects where structure matters, like complex admin panels, because it comes with built-in tools for testing and security.
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React.js is our go-to framework for interactive, neat UIs that customers actually enjoy using. We pick it because reusable components make code updates easy, and it scales beautifully for enterprise apps.
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Since Vue.js is very lightweight and flexible, it lets us start small and scale up without rewriting everything. Vue has an easy learning curve, so we pick it for clients who need quick prototypes and keep projects fast.
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Flutter lets our team build beautiful iOS and Android mobile apps from one codebase, which saves almost 50% of the work. We go with it for its smooth animations and native feel, perfect for cross-platform apps.
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Using React Native, we create cross-platform mobile apps that feel native and drive top-notch UX. It cuts costs and speeds up launches, which is huge for businesses wanting apps yesterday.
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Django has everything for our software developers to build high-quality web apps, from security to admin dashboards. We love building secure, scalable platforms fast with it, perfect for data-driven projects.
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We frequently use Express.js as it keeps our Node backends simple and speedy with minimal setup. It helps us create flexible APIs that scale, are easy to tweak as needed, and handle real-world traffic.
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.NET (including .NET Core) gives our team a rock-solid foundation for building multi-platform backends. We like working with it because it integrates easily with cloud environments and scales predictably.
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Next.js takes our React builds to the next level, as it makes websites load lightning-fast with smart server-side tricks. We love how it handles SEO for high-performance sites that rank well and convert visitors.
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Laravel makes PHP development elegant and fun with smart tools. We use it for fast app launches and easy maintenance. Its ecosystem saves hours and lets us deliver secure, beautiful solutions that scale effortlessly.
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Our developers lean on NestJS to bring order to Node.js projects with a clean, modular design. We prefer it because teams can build without hassle. It’s perfect for APIs that handle high traffic.
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SwiftUI makes iOS app development a joy for our Apple specialists. It’s simple, visual, and adaptive to any device. We frequently chooses it to create intuitive apps with less code and faster previews.
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FastAPI is the framework we use to create blazing-fast APIs in Python, with easy docs and validation that come free. Our dev team uses it for data-heavy services because it handles massive loads effortlessly, from AI backends to analytics.
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We use Spring Boot to simplify Java enterprise apps with auto-setup. It’s our primary choice for mission-critical systems and high-stakes enterprise software that need to be well-architected and scalable.
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Rails is our favorite for developing full-featured web apps super quickly. We love how it follows smart conventions that keep code clean and fast, perfect for startups scaling to enterprise without rewrite nightmares.
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RxWeb.io is our in-house built open-source framework for Angular and .NET Core-based products. Our team extensively works with it to build enterprise apps that need domain-driven design and microservices.

TensorFlow remains our default ML framework. We choose it to build ML pipelines and optimized inference on heterogeneous hardware. It helps us keep model exports stable and deployments consistent without rebuilding core training logic.
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At the heart of our AI experiments is PyTorch. As a research-to-production ML framework with strong ecosystem and model-ops support, we use it for prototyping and innovation. Great for enterprises pushing AI boundaries with confidence.
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AI engineers at Radixweb adore HFT for making cutting-edge AI models super accessible and ready to use right away. It’s perfect for businesses wanting to build AI without years of training from scratch and ship updates in a controlled, dependable pace.
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Snowflake makes data warehousing effortless and cost-smart for us. We love its flexibility as engineers can scale compute and share data securely. It also helps us maintain clear data access paths for stakeholders and keep cost traceable by workload.
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We use dbt, a data transformation framework, for SQL-first data engineering and analytics engineering. It’s a game-changer for building trustworthy analytics pipelines that fuel smarter business decisions without the usual headaches.
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TezJS is our pick for lightning-fast Jamstack sites powered by Vue.js. It packs everything needed for high-performance web apps, delivers fast load times that keep users engaged, and boosts SEO.
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Spark crunches massive datasets at speed for our big data needs. Developers at Radixweb use it to process insights in minutes from huge data lakes without the hassle and deliver consistent results that businesses can trust. It's our top pick for analytics.
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We chose Kafka because it efficiently handles data streams across multiple projects without failing under load. It connects our apps directly and moves data from sources to analytics without custom code. For us, it cuts setup time and keeps everything running day-to-day.
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Apache Airflow fits our needs for managing data jobs that repeat daily. We picked it after trying others because our engineers can define workflows in code and monitor them from one dashboard. Works well for us handling daily reports and batch jobs across teams.
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Our developers use JAX when product and AI experiments need faster iteration cycles and reliable math computation behind the scenes. It helps our team test models efficiently, keep results consistent, and support large-scale learning projects without rebuilding the workflow.
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We work with scikit-learn when enterprises need applied machine learning in business data. Our engineers train models with it for predictions and clustering without extra setup. It handles standard tasks like classification and saves time on our data science consulting projects.
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Not sure which framework you need? Tell us your goals and we'll recommend the ideal stack. Or let our team audit your specs for a custom pick.