Legacy UI Modernization with Chiron

Chiron — Agentic Delivery Case Studies. How Chiron enables hybrid human-agent software teams to plan, build, modernize, and automate complex workflows faster, with fewer people and lower cost.

The client needed to modernize legacy user-interface pages into its newer frontend architecture. The work required more than a visual rewrite. The pages had to be rebuilt using the correct the new frontend structure, aligned with the component strategy, migrated toward reusable design tokens and components, and prepared for integration into the broader monorepo.

The modernization scope included Domain Profiles, the Recipient Profiles tab, and a connected Policies page. These pages had to be updated while preserving functional behavior, improving consistency, increasing reuse, and supporting performance and quality requirements such as load speed, Core Web Vitals, bundle analysis, functional tests, i18n validation, and dependency validation.

The key challenge was delivery speed. The client had previously spent approximately 14 days migrating one page without AI. Using AI platforms such as Devin reduced that effort to around five days for one page. Taller needed to show that Chiron could go further: not just accelerate an individual developer, but compress the full modernization workflow across multiple pages through coordinated human-agent delivery.

Taller used Chiron to orchestrate the modernization project as a hybrid human-agent workflow.

The Knowledge Database gave the Chiron agents access to the relevant technical context: page scope, frontend patterns, reference implementations, the design system component usage, coding guidelines, dependency expectations, and modernization requirements. Instead of asking an AI coding tool to operate from a limited prompt, Chiron grounded the engineering agents in the same project context the human team was using.

The planner decomposed the modernization work across the three-page scope. It identified the shared patterns between Domain Profiles, Recipient Profiles, and the connected Policies page, allowing the team to avoid treating each page as an isolated rewrite. Repetitive logic was moved toward reusable structures in the shared component layer, and UI elements were migrated toward the design system components and design-token alignment.

The engineering agents operated from the developer terminal through Chiron's CLI, using familiar coding tools while remaining connected to the Chiron web platform, shared plan, shared taskboard, and institutional knowledge layer. This allowed the human team to coordinate work, review progress, and maintain visibility across the modernization effort.

Chiron's Pelion workflows supported quality and reliability. Multiple agents could reason over the same implementation paths, compare outputs, validate against project rules, and help reduce the risk of hallucinated or inconsistent code. Human engineers remained responsible for review and architectural judgment, while agents accelerated implementation, refactoring, and validation.

Using Chiron, Taller migrated three the legacy pages in two days. By comparison, a major cloud security company had previously spent approximately 14 days to migrate one page without AI, and approximately five days to migrate one page using AI platforms such as Devin.

The Chiron-based approach demonstrated a step-change in modernization throughput: three pages in two days, with the work structured around reusable components, shared patterns, quality artifacts, and readiness for integration into the modernized frontend architecture.

The a major cloud security company project demonstrated Chiron's value in frontend modernization, especially where the work is repetitive enough for agents to accelerate but complex enough to require real engineering judgment.

The strategic significance is that Chiron did not simply generate code faster. It changed the modernization operating model. The platform helped centralize context, plan the work, coordinate human-agent execution, reuse common logic, and maintain quality through structured agentic workflows.

For enterprise software teams with large legacy frontend estates, this pattern is highly repeatable. Chiron can help compress modernization timelines, reduce the team required for migration work, and keep quality high by combining AI execution with shared planning, institutional context, and human review.