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Case Study: Workbench AI

Workbench AI - Accelerating Enterprise AI

Workbench AI, co-developed by Naga, is a no code AI builder that redefines how enterprises build, train, and deploy intelligent models. Designed to remove barriers to AI adoption, the platform allows teams to prototype and operationalize projects without writing a single line of code.

Naga’s experience in AI development, IT services, and software outsourcing shaped the platform’s architecture, scalability, and AI software design. Together with the Kranium AI team, Naga delivered a system that helps enterprises create and deploy production-grade models faster and at a fraction of traditional cost.

Agile and Rapid Prototyping: From Vision to Product

To transform the vision of a fully no code AI builder into a working product, Naga’s engineering team adopted an agile, sprint-driven approach. Short feedback loops, rapid prototyping, and continuous iteration guided the development process.

The project began with discovery workshops where business goals were mapped into technical milestones. Each sprint delivered a working prototype, from dataset upload and training screens to dedicated dashboards. Instead of long wait cycles, stakeholders interacted with live builds within weeks, shaping features through direct feedback and real usage.

This approach enabled drag-and-drop workflows, one-click deployment, and integrated analytics to grow directly from user insights. By prioritizing validation over lengthy documentation, Naga ensured each feature addressed a real need, reducing time-to-market and delivery risk.

For Naga, the platform became a showcase of AI software design, combining technical precision with adaptive delivery to achieve innovation at enterprise scale.

Microservices Architecture for Infinite Scalability

Scalability and flexibility were foundational to Workbench AI’s design. Naga architected the platform around a microservices framework, ensuring each service could scale independently and use the most suitable technology for its function.

Instead of a single monolithic system, the platform was divided into specialized services, data ingestion, data processing, model training, inference, user management, and deployment monitoring. Each module was built using the technology best suited to its role:

  • Python / Flask for machine learning services,

  • Node.js for lightweight APIs,

  • Go for streaming and concurrent tasks, and

  • Kubernetes + Docker for container orchestration and seamless scalability.

This approach ensures Workbench AI can scale easily for more users, larger datasets, or updated model libraries without disruption. Enterprises gain a system that grows as their needs grow, supported by proven AI web development patterns applied to microservices.

This architectural discipline positioned Workbench AI as a benchmark for how to develop AI software built for enterprise scale, fault-tolerance, and continuous evolution.

 

Custom UX Designed from the Ground Up

For a no code AI builder platform to succeed, usability is everything. Naga’s design and front-end engineering teams built a completely custom user experience from scratch, not just a themed dashboard, but a workflow-oriented design that simplifies complex AI operations into intuitive visual steps.

The UX design process began with user-journey mapping, identifying how data scientists, analysts, and business users would interact with the system. Each screen was then prototyped, tested, and refined to ensure clarity and speed.

The result is an interface where users can:

  • Upload and prepare data through guided steps,

  • Choose models from a visual library,

  • Train and evaluate results in real time,

  • Deploy models instantly as APIs or web services.

Modern front-end frameworks like React JS were used to ensure smooth interactivity, while contextual tooltips, visual flows, and error-proofing improved usability.

As a company experienced in AI web development, with a deep focus on human-centered design, Naga delivered a UX that lowers the technical barrier, enabling anyone, from analysts to executives, to harness the power of AI without specialized training.

Multi-Database Architecture for Speed and Efficiency

One of the technical highlights of Workbench AI is its multi-database architecture – a polyglot persistence strategy where each data type is stored in the system best optimized for it.

Naga’s engineers implemented multiple database layers, including:

  • PostgreSQL for transactional data and relational models,

  • MongoDB for flexible document-based metadata and configuration,

  • Redis for caching and fast data retrieval,

  • S3-compatible object storage for datasets, logs, and model files, and

  • Time-series databases for monitoring model performance over time.

This design ensured every subsystem operated with optimal performance and scalability. By separating data concerns and aligning technology to purpose, the platform achieved rapid response times and high reliability even under heavy enterprise workloads.

Such architectural finesse reflects Naga’s maturity as a team that can develop AI software balancing data speed, flexibility, security, and maintainability.

Enterprise Benefits: Speed, Savings, and Simplicity

The ultimate success of Workbench AI lies in what it delivers to its users: a way for enterprises to deploy AI solutions faster and more affordably than ever before.

Traditionally, developing AI systems required months of setup, extensive data engineering, and dedicated data-science teams. Workbench AI collapses this timeline from months to weeks, sometimes even days. Using pre-built workflows, model templates, and automated training pipelines, enterprises can:

  • Integrate their datasets instantly,

  • Experiment with multiple AI models,

  • Deploy predictive APIs within clicks, and

  • Monitor performance without manual maintenance.

By eliminating heavy engineering cycles through a no code AI builder, enterprises reduce costs by more than 60% while gaining agility to experiment, scale, and improve continuously.

This efficiency advantage defines why Workbench AI stands out as a cornerstone of enterprise AI development, enabling large organizations to leverage AI’s potential without high technical overhead.

Security, Deployment, and Maintenance

Given its enterprise clientele, Workbench AI was designed with security and compliance at its core. Each microservice enforces authentication and role-based access control, ensuring isolation between tenants and data sources.

Automated deployment pipelines with CI/CD ensure smooth updates and zero downtime. Continuous monitoring with real-time logs, alerts, and metrics visualization enables proactive maintenance, all built into the platform’s microservice fabric.

Whether deployed on private cloud, on-premises, or hybrid environments, the platform adapts to enterprise requirements, leveraging Naga’s experience in AI web development for reliable deployment in complex infrastructures.

Conclusion

Workbench AI demonstrates how the right engineering partner can transform an idea into enterprise innovation. Through agile delivery, scalable microservices, multi-database architecture, and human-centered UX, Naga helped shape a no code AI builder that shortens deployment time while maximizing value.

For enterprises looking to develop AI software faster and more affordably, Workbench AI, co-developed by Naga, proves that building intelligent systems doesn’t need to be complex or costly. It simply needs to be built smart.

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