Fei

Product Introduction

1. Fei is an autonomous engineering AI designed to generate production-grade code directly within existing codebases while adhering to organizational design systems and coding standards. It operates as a collaborative tool for development teams, automating repetitive coding tasks and translating high-level requirements into deployable solutions. The system integrates with version control systems and component libraries to maintain consistency across projects. It supports frontend and backend development by interpreting schemas, Figma designs, screenshots, or text inputs to generate API-aware code.

2. The core value of Fei lies in its ability to eliminate manual coding bottlenecks while preserving engineering quality and architectural integrity. It enables teams to focus on strategic problem-solving by automating 40-60% of routine development work, as evidenced by enterprise use cases like Deeto’s 40% reduction in screen development time. By enforcing design system compliance and component reuse, it ensures UI/UX consistency across distributed teams. The platform reduces dependency on specialized tools like Figma for design-to-code translation, as demonstrated by Mending Health’s production-ready sidebar implementation in 20 minutes without design files.

Main Features

1. Fei autonomously generates API-integrated code by parsing OpenAPI schemas or database models, ensuring compatibility with existing backend services. It constructs endpoints, data models, and frontend bindings that align with organizational security protocols, as highlighted in compliance documentation referencing Data Processing Agreements. This feature supports real-time collaboration between frontend and backend teams through automated contract enforcement.

2. The system ingests Figma files, screenshots, or textual descriptions to produce pixel-perfect UI components using predefined design system tokens. It maps design elements to existing React/Vue components in the codebase, as seen in Commit’s implementation where junior developers delivered senior-level output. Dynamic layout adaptation ensures responsiveness across breakpoints while maintaining accessibility standards like WCAG 2.1.

3. Fei implements continuous code quality checks through customizable rule sets that enforce linting rules, test coverage thresholds, and architectural patterns. It automatically generates unit tests matching the project’s Jest/Cypress configuration and identifies component reuse opportunities, as utilized by Mantra to maintain UI consistency across microfrontends. The AI refactors legacy code using approved migration strategies documented in the codebase’s ADRs (Architectural Decision Records).

Problems Solved

1. Fei addresses the costly disconnect between design prototypes and implemented code, eliminating manual translation errors that consume 15-30% of development cycles. It solves version drift between Figma files and actual components, as demonstrated by Novella’s accelerated workflow where engineers shifted focus from UI implementation to feature logic.

2. The product specifically targets mid-to-large scale engineering organizations managing multiple parallel product streams, particularly those with established design systems struggling with cross-team consistency. Enterprise users like Google Cloud Security teams benefit from its ability to scale coding standards enforcement across distributed contributors.

3. Typical use cases include rapid prototyping of customer-facing features using existing component libraries, legacy system modernization through automated refactoring, and emergency hotfix implementation with built-in regression testing. MindHub’s deployment shows how non-technical product managers can directly initiate frontend changes while maintaining code quality guardrails.

Unique Advantages

1. Unlike generic AI coding assistants, Fei deeply integrates with organizational tech stacks through predefined adapters for Webpack configurations, CI/CD pipelines, and monorepo management tools. It maintains context awareness through continuous analysis of commit histories and architectural documentation, enabling context-sensitive code generation that respects domain boundaries.

2. The platform introduces patented "vibe coding" technology that interprets ambiguous requirements through probabilistic alignment with existing patterns in the codebase. This enables handling of incomplete specifications while maintaining production readiness, as shown in Commit’s experience where Fei outperformed both manual coding and alternative AI tools in output quality.

3. Competitive differentiation comes from Fei’s compliance-first architecture, featuring enterprise-grade security controls like SOC 2-compliant data isolation and on-prem deployment options. The system’s learning loop incorporates code review feedback to continuously improve its rule sets, creating institutional knowledge retention that surpasses human team turnover rates.

Frequently Asked Questions (FAQ)

1. How does Fei ensure generated code matches our existing architecture? Fei performs continuous repository analysis to understand your codebase's patterns, dependency graphs, and ADRs, ensuring all outputs align with approved architectural standards. It cross-references component libraries and API contracts before generating any code.

2. Can Fei work with proprietary design systems not using Figma? The system supports multiple input formats including Storybook components, PDF specifications, and even hand-drawn wireframes through computer vision analysis. Custom parser configurations can be defined through YAML-based mapping files.

3. What security measures protect sensitive codebases? Fei operates within your existing infrastructure using Docker-based isolation and supports air-gapped deployments. All data processing complies with GDPR and CCPA through configurable consent management, as referenced in the Data Processing Agreement documentation.

4. How does the system handle legacy tech stacks? The AI employs incremental modernization strategies, first generating compatibility layers before refactoring legacy components. It automatically creates migration tests and rollback plans, as utilized in Mantra’s legacy Angular to React transition.

5. What integration requirements exist for CI/CD pipelines? Fei provides plugins for GitHub Actions, GitLab CI, and Jenkins that inject quality gates at pull request stages. The system requires read access to repository contents and design system packages to maintain context-aware code generation.