Get Started
Get Started
Get Started

Fei is unique in category

Fei Studio and Fei IDE unite design, product and development into a single workflow, that accelerates organizations and individuals.
Speak to Sales

Compare

Why Fei  and Cursor are better together

Cursor boosts developer speed. Fei Studio lets product teams request changes and delivers production-ready updates. Used together, teams ship faster and with less friction.

Feature
Core Functionality
AI Assistant Type AutonomyAI is a system-level agent that operates across files and patterns. Cursor is an IDE assistant focused on developer productivity inside a file.
System-Level Execution AutonomyAI can plan and implement multi-file, multi-component updates from a single instruction. Cursor is optimized for file-focused edits and rapid iteration, not cross-system orchestration.
Developer Workflow Integration Cursor integrates directly in the IDE for fast editing. AutonomyAI integrates at the repo and system level for feature development.
No-Code / Low-Code Product Team Support AutonomyAI enables PMs, designers, and UX writers to request real product changes without touching code. Cursor requires engineering knowledge and IDE usage; built strictly for developers.
Inputs
Natural Language Instructions (Simple Prompts) AutonomyAI requires no codebase knowledge. Cursor benefits from developer awareness of file structure.
Visual Inputs (Screenshots, UI Regions) AutonomyAI can identify components onscreen and map them to code. Cursor does not map UI screenshots to component structures.
Ticket / Workflow Integration (Jira, Task Mapping) AutonomyAI links tasks, prompts, and code delivery in one workflow. Cursor does not natively connect prompts to task systems.
Codebase Integration
Cross-File Reasoning & Refactoring AutonomyAI analyzes and updates multiple files, flows, and layers in one operation. Cursor operates primarily inside the active file unless manually guided.
Component & Pattern Reuse Awareness AutonomyAI understands and reuses design systems, shared components, and internal patterns automatically. Cursor reuses patterns only with developer guidance, not autonomously.
Adherence to Repo Configuration (ESLint, TSConfig, conventions) AutonomyAI writes code aligned with existing rules and architecture.
Holistic SDLC Execution (ACE Engine) AutonomyAI executes planning, component selection, data modeling, validation logic, and code generation in one shot — no complex prompting required. Cursor is a coding copilot; it does not simulate or execute the full engineering lifecycle.
Enterprise
Built for Teams (PM, Design, Dev Collaboration) AutonomyAI enables multiple roles to drive product changes safely. Cursor is an individual developer tool inside the IDE.
Production-Grade Output (Merge-Ready Code) AutonomyAI’s code is generated through ACE, maintaining repo patterns, structure, and correctness.
Ideal Use Case Fit Cursor accelerates individual implementation. AutonomyAI accelerates team-level shipping and system integrity.

Compare

Why choose Fei  over Lovable?

Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects

Feature
Core Functionality
Code Generation Both tools can generate UI code from natural language prompts. AutonomyAI continues from there into deeper codebase integration.
Code Update AutonomyAI can modify real files in your repo while preserving structure and project standards. Lovable does not support editing existing files in the codebase.
Visual Output & Mocked Data Both platforms support visual previews and mock states. AutonomyAI emphasizes realistic multi-state simulation.
Inputs
Jira Integration AutonomyAI ties tasks to code, allowing structured delivery and traceability. Lovable has no public support for task management or issue-tracking integration.
Figma Integration Both platforms import designs from Figma. AutonomyAI maintains structural and interaction logic.
Codebase Integration
Codebase Integration AutonomyAI works directly in your repo with full version control support. Lovable runs in its own cloud IDE. It does not sync directly with your existing repo structure.
Custom Component Reuse AutonomyAI reuses your components for visual and structural consistency. Lovable does not import or reuse your existing design system or component library.
Component Reuse Across Screens AutonomyAI detects and reuses similar components across user flows. Lovable does not track or reuse UI patterns across screens or projects.
Custom Library Support AutonomyAI understands internal packages, utility functions, and hook usage. Lovable does not ingest or model custom libraries or utilities.
Guardrails from Project Configuration AutonomyAI respects your lint rules, TypeScript config, and project conventions out of the box. Lovable does not align its output to your repo's existing rules or tooling.
Enterprise
Built For Teams AutonomyAI supports collaboration, shared ownership, and delivery pipelines across team environments. Lovable is optimized for solo builders and individual use—not team collaboration or review cycles.
End-to-End Task Stitching AutonomyAI unifies prompt, design, task, and code into one cohesive workflow with traceability. Lovable does not offer structured workflow stitching or task traceability across systems.
Production-Grade Output AutonomyAI outputs clean, documented, and review-ready code intended for production deployment. Lovable’s output is optimized for prototyping—not direct production use.
Structured Work Summary AutonomyAI generates README-style summaries documenting what was done and what’s left to complete. Lovable does not generate documentation, summaries, or structured meta around output.

Compare

Why choose Fei  over Claude Code + Figma MCP

Claude Code boosts developer speed. AutonomyAI handles system-level updates and supports product teams directly. Together they help teams code faster and ship faster.z

Feature
Core Functionality
AI Assistant Type AutonomyAI is a system-level engineering agent. Claude Code is a coding assistant; MCP connects tools for context-aware operations.
System-Level Implementation AutonomyAI plans, coordinates, and implements multi-file, multi-component features end-to-end from a single request. Claude Code focuses on file-level edits; MCP enables tool access but does not orchestrate holistic engineering workflows.
Product Team Usability (PM, Design, UX) AutonomyAI allows non-engineers to request production changes safely. Claude Code requires technical knowledge; MCP still depends on engineering-led workflows.
Inputs
Natural Language Instructions (Simple Prompts) AutonomyAI requires no prompting skill or codebase knowledge. Claude Code benefits from explicit developer guidance.
Visual Understanding (UI Screenshots / Regions) AutonomyAI maps screenshots to components and code in the repo automatically. Claude MCP does not map visual elements directly to code structures.
Figma Integration (MCP) MCP enables Claude to access Figma files. AutonomyAI converts Figma designs into production-ready code aligned with the repo.
Codebase Integration
Cross-File Reasoning & Execution AutonomyAI updates multiple files, components, and logic paths from one request. Claude Code focuses on individual files; multi-file changes require explicit step-by-step direction.
Component & Pattern Reuse Awareness AutonomyAI automatically reuses existing design-system components and shared utilities. Claude does not autonomously map repo-wide component patterns without developer guidance.
Holistic Front-End Engineering (ACE Engine) AutonomyAI executes validation logic, state modeling, data flow, component selection, and architecture alignment behind the scenes. Claude generates code but does not execute a full engineering lifecycle automatically.
Repo Configuration Compliance (TSConfig, ESLint, conventions) Both tools can follow conventions when guided, but AutonomyAI enforces them automatically per repo.
Enterprise
Built for Teams (PM, Design, Dev Collaboration) AutonomyAI supports cross-functional workflows across product, design, and engineering. Claude Code is built for developers, not cross-functional execution.
Production-Grade Output (One-Shot, Merge-Ready) AutonomyAI generates feature-complete, architecture-consistent code ready for PR review.
Ideal Use Case Fit Claude Code accelerates developer coding. AutonomyAI accelerates team-level shipping and system-wide coherence.
“Better Together” Fit with Claude MCP Tools Claude + MCP provide strong code editing and tool access. AutonomyAI handles higher-level orchestration. Together they offer both per-file speed and system-level execution.

Compare

Why choose Fei  over V0?

Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects

Feature
Core Functionality
Code Generation Both tools generate UI from natural language prompts. AutonomyAI builds into production-grade systems.
Code Update AutonomyAI works based off your actual codebase, delivering usable results not just the sandbox demoes. V0 generates new code in isolation; it doesn't update existing files in real codebases.
Visual Output & Mocked Data Both platforms offer visual previews. AutonomyAI adds data-state simulation and logic-aware mocking.
Inputs
Jira Integration AutonomyAI integrates with ticketing tools to link tasks with code changes. V0 has no native integration with Jira or other task managers.
Figma Integration Both tools support Figma import. AutonomyAI extends it with layout fidelity and reusable logic.
Codebase Integration
Codebase Integration AutonomyAI commits directly to your version-controlled repo. V0 doesn’t operate inside your repo; output must be manually transferred.
Custom Component Reuse AutonomyAI reuses your components and respects existing structure. V0 does not map or reuse your component library; outputs are always fresh.
Component Reuse Across Screens AutonomyAI identifies repeating UI logic and patterns. V0 generates isolated screens without pattern linking or reuse.
Custom Library Support AutonomyAI uses your internal helpers, hooks, and libraries during generation. V0 doesn't understand or integrate with your custom code modules.
Guardrails from Project Configuration AutonomyAI respects your config, including ESLint, Prettier, and TS rules. V0 has no repo-aware linting or configuration alignment.
Enterprise
Built For Teams AutonomyAI supports PR flow, handoffs, team collaboration, and role separation. V0 is focused on individual creators or demos, not team workflows.
End-to-End Task Stitching AutonomyAI connects prompts to code, designs, and tickets in one chain. V0 does not link its output to task context or user stories.
Production-Grade Output AutonomyAI generates reviewable, mergeable, and scalable code artifacts. V0’s code is demo-friendly but requires rework before production use.
Structured Work Summary AutonomyAI outputs a readable summary of changes and reasoning per task. V0 doesn't document output or rationale for the code it generates.

Compare

Why choose Fei  over Replit

Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects

Feature
Core Functionality
Code Generation Both platforms allow natural language to code workflows. AutonomyAI is tailored to UI components; Replit focuses on general-purpose coding via Ghostwriter and Agent.
Code Update AutonomyAI updates real files in your repo. Replit Agent can modify code but lacks structured, config-aware updates.
Visual Output & Mocked Data Both platforms support previews. AutonomyAI simulates multi-state UI; Replit emphasizes deployment and live view.
Inputs
Jira Integration AutonomyAI links tasks to code for delivery traceability. Replit has no native Jira support.
Figma Integration AutonomyAI imports structured UI from Figma. Replit does not offer design file ingestion.
Codebase Integration
Codebase Integration AutonomyAI works inside your repo. Replit uses its own cloud-based environment with GitHub syncing.
Custom Component Reuse AutonomyAI reuses and references your design system. Replit doesn’t support reusable UI components out of the box.
Component Reuse Across Screens AutonomyAI maps UI patterns across flows. Replit doesn’t detect or leverage screen-level UI reuse.
Custom Library Support AutonomyAI supports hooks and shared logic. Replit treats all code as project-local unless manually managed.
Guardrails from Project Configuration AutonomyAI respects linter and build settings. Replit doesn’t enforce config-based safety rules across runs.
Enterprise
Built For Teams AutonomyAI enables role-based workflows and team collaboration. Replit Teams offers multi-user support but lacks deeper enterprise workflows.
End-to-End Task Stitching AutonomyAI connects design, task, and implementation. Replit Agent can scaffold projects but lacks structured traceability.
Production-Grade Output AutonomyAI outputs clean, deployable code. Replit can deploy code but has had incidents where AI suggestions caused critical errors.
Structured Work Summary AutonomyAI documents what was done and why. Replit Agent doesn’t produce structured logs or summaries per run.

Compare

Why choose Fei  over Base44

Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects

Feature
Core Functionality
Code Generation Both tools generate UI from natural language prompts. AutonomyAI extends the process into your actual codebase.
Code Update AutonomyAI edits live repo files with full structural awareness. Base44 lacks persistent context and doesn’t reliably update existing files in structured repos.
Visual Output & Mocked Data Both platforms support visual previews. AutonomyAI adds dynamic state modeling.
Inputs
Jira Integration AutonomyAI connects tickets to code delivery. Base44 lacks built-in Jira or issue tracker integration, requiring manual context stitching.
Figma Integration Both tools import from Figma. AutonomyAI preserves interaction logic and naming conventions.
Codebase Integration
Codebase Integration AutonomyAI reads your and writes based on your existing infra and components Base44 outputs must be copied manually; no direct codebase sync.
Custom Component Reuse AutonomyAI references and reuses your existing components. Base44 lacks awareness of your existing component library.
Component Reuse Across Screens AutonomyAI remembers design patterns and applies them throughout your app. Base44 treats every screen as a fresh task without context reuse.
Custom Library Support AutonomyAI understands and applies your internal utilities and shared libraries. Base44 doesn’t reuse or reference custom libraries from your codebase.
Guardrails from Project Configuration AutonomyAI adheres to your ESLint, TSConfig, and naming rules. Base44 does not follow your repo’s configuration or code quality rules.
Enterprise
Built For Teams AutonomyAI is designed for collaborative work environments. Base44 was built for solo makers; lacks multi-user support.
End-to-End Task Stitching AutonomyAI merges prompt, design, planning, and code in a single workflow. Base44 doesn’t connect prompt-to-code to task or design context.
Production-Grade Output AutonomyAI delivers merge-ready, linted, testable code. Base44 outputs raw, often untested code intended for demo use.
Structured Work Summary AutonomyAI provides a clear, human-readable log of what was done. Base44 does not output explanations, rationales, or logs alongside code.
Mending Health created a production ready sidebar for their customer dashboard in 20 minutes. No figma
Request Beta Access
Deeto enabled Product Managers to handle frontend development. Overall screen development time dropped by 40%
Request Beta Access
Commit standardized component reuse across teams-ensured consistent UI and enabled junior devs to ship senior-level code.
Speak to Sales

Don't take our word for it.
Take Theirs.

AutonomyAI is creating a real impact when it comes to managing your R&D. The focus on delivering production grade results is helping companies move faster and more efficiently"

Vikram MakhijaSr. Director R&D Google Cloud Security

AutonomyAI helped us move faster as a business and increase our production velocity in a very competitive market” 

Eran BaronCEO and Co-Founder at Deeto

AutonomyAI’s leadership team’s vision represents a fundamental shift in how organizations use AI, which will soon become the new standard.” 

Gilad Shanyinvestor and Managing Partner at ION Crossover

Using AutonomyAI helped me focus on the main things in my workflow and accelerate the front end development”

Daniel Aziz Full Stack Engineer Novella

Unify Design, Product and Dev

Into a single, powerful, AI Native workflow

Speak to Sales

FAQ

Questions & Answers

Answers to questions about how Fei works, where it runs, and how it operates in the enterprise environment.

Fei Studio is where product teams, designers, and engineers collaborate directly on the real product. It generates working components, documentation, and updates in your actual codebase. Deployed as a web based application, making it easily accessible for non engineering users.

Fei IDE is the engineering environment built around Fei’s autonomous abilities. It analyzes your codebase, proposes changes, creates new components, and integrates them directly into your repository. 

Fei IDE is deployed as an extension in Cursor VSCode or any other fork.

Fei Studio sets the product direction. Fei IDE translates that direction into real implementation. Both run on the same internal understanding of your system, so product and engineering stay aligned without handoff gaps.

ACE is the Agentic Context Engine, the internal intelligent system Fei Studio and Fei IDE use to understand your entire product and codebase. It provides structured, deterministic context so Fei’s reasoning is stable and predictable.

No. ACE runs behind Fei Studio and Fei IDE. You don’t maintain or configure it—your team simply benefits from the accurate system understanding it provides.

Yes. Fei reads your real repository, services, and configuration. It doesn’t require migrations, rebuilds, or architectural changes.

Fei operates in an enterprise-grade security model with strict isolation, minimal data retention, and fully auditable workflows.

Yes. Our platform—including Fei Studio, Fei IDE, and ACE’s backend infrastructure—is SOC2 certified.

All processing happens inside our secure execution environment with controlled access paths. We do not use your data for training or external purposes.

Private deployments are available for organizations with strict isolation or regulatory requirements. Speak to sales for further information.

Fei plugs directly into your repositories, design tools, and product processes. There are no workflow changes—only fewer handoffs and less overhead.

Yes. ACE was designed specifically for realistic systems, including legacy, fragmented, or partially documented repositories.

You can request access to Fei Studio Beta or speak to sales about company and enterprise programs.

Fei Studio is the workspace where product, design, and engineering collaborate directly on the real product. It lets teams define behavior, UX, flows, and component intent—then generates production-ready outputs aligned with the codebase, ready for developer approval.

Unlike traditional vibecoding tools, Fei Studio operates on existing, enterprise level products. Its output is senior developer level aligned to company standards, design systems and even tribal knowledge – code that is ready for approval,

Cursor and Claude are AI coding assistants designed for developers. They speed up coding inside an IDE, but they still rely on the user to know the codebase, locate the right files, and understand what production-grade code looks like.

Fei Studio works at a different layer entirely. It’s an AI native workflow for the whole product team. PMs, designers, and UX writers can open a screen, describe a change, and Fei handles the rest – finding the component, understanding the repo, and generating developer-ready updates.

Unlike AI assistants and co-pilots, Fei Studio doesn’t require complex prompting or any knowledge of the codebase. Our ACE engine carries all the contextual understanding, and Fei Studio executes the full front-end engineering lifecycle behind the scenes. The result is production-grade code in one shot, ready for developers to review and approve.

 

Fei Studio runs on ACE, the intelligent context management engine that maintains an accurate model of your repository, design system, components, API surfaces, and product logic.

No. Fei Studio works with your existing design tools and design system. It translates product intent into structured outputs Fei IDE can implement in the actual codebase.

Studio can produce any UI element you can describe. From buttons, to modals, to layouts, full pages, or even multipage flows with tables, graphics, unique user flows, UX logic, validations, and more.

When finished, the end result is high quality code, ready for developer approval.

Yes. Studio was designed for PMs, designers, and domain experts – giving non technical team members autonomy to not only prototype but actually build functional products within the actual product frameworks and standards, ready for developer approval.

Studio creates structured product intent; IDE turns it into code, tests, and updates in your repository. Both share the same context model through ACE, so there are no handoff gaps.

No. Fei does not perform any form of centralized or federated training on customer data. It uses static analysis and annotation map alongside embeddings generated per project but does not store any customer code.