UX Research

Meet Our Users

To build a truly effective AI code translation tool, we must first understand the people who will use it. Based on our foundational research, we've developed three key personas that represent our target audience.

Headshot of Eleanor Vance

Eleanor Vance

Senior Enterprise Developer

"My team manages millions of lines of legacy code. Manual migration is not just slow, it's risky. I need a tool I can trust to get the nuances right."

Background

Eleanor is a lead software architect at a top-tier financial institution. With 18 years of experience, she oversees critical backend systems built on aging tech stacks. Her primary responsibility is ensuring system stability while planning and executing gradual modernization projects to mitigate technical debt and improve performance.

Goals & Motivations

  • Modernize legacy systems (e.g., COBOL/Java to Python/Go).
  • Increase team efficiency and reduce development cycles.
  • Ensure functional equivalence and performance post-migration.
  • Reduce risks associated with manual code rewriting.

Frustrations

  • Manual translation is tedious, error-prone, and unscalable.
  • Current AI tools lack the accuracy for mission-critical code.
  • Difficulty ensuring the translated code is idiomatic and maintainable.
  • Managing complex dependencies and library incompatibilities.

Technical Skillset

Java Python COBOL Go System Architecture Database Management CI/CD
Headshot of Marco Rossi

Marco Rossi

Freelance Web Developer

"I juggle clients with completely different tech stacks. A reliable converter would be a game-changer, saving me hours of context-switching and boilerplate work."

Background

Marco is a versatile full-stack developer who thrives on variety. He runs his own freelance business, taking on projects from small business websites to complex web applications. He often needs to integrate new features into existing codebases or migrate parts of a project from one language to another to meet client demands.

Goals & Motivations

  • Deliver high-quality projects on time and budget.
  • Quickly adapt to different frameworks and languages.
  • Maximize billable hours by automating repetitive tasks.
  • Stay competitive by using modern, efficient tools.

Frustrations

  • High cognitive load from constant context-switching.
  • Time wasted on rewriting simple logic for different languages.
  • Integration challenges between multi-language components.
  • Finding trustworthy tools that don't introduce subtle bugs.

Technical Skillset

JavaScript React Node.js PHP Python SQL & NoSQL AWS / Vercel
Headshot of Priya Sharma

Priya Sharma

Computer Science Student

"Switching between Java for my data structures class and Python for my AI project is so confusing. I wish there was a tool that didn't just convert the code, but explained the differences."

Background

Priya is a bright and ambitious third-year computer science student. She is passionate about programming but finds it challenging to juggle the different syntaxes and paradigms of the languages she's learning for her courses. She spends her time on assignments, personal projects for her portfolio, and preparing for internships.

Goals & Motivations

  • Understand core differences between programming languages.
  • Quickly convert snippets for assignments and projects.
  • Build a strong portfolio to secure a good internship.
  • Learn programming concepts more efficiently.

Frustrations

  • Difficulty grasping different programming paradigms.
  • Getting stuck on syntax when switching between languages.
  • Tools that convert code without explaining the logic.
  • Lack of accessible resources for comparing code side-by-side.

Technical Skillset

Python Java C++ JavaScript Data Structures Git & GitHub