CodeFlow is an AI-first development platform that generates production-ready full-stack applications from text prompts or Figma designs. We support React, Flutter, and TypeScript with built-in authentication, databases, and deployment pipelines.

How does AI code generation work?

Simply describe your application in plain English, and our AI generates a complete full-stack codebase with frontend, backend, database schemas, and deployment configuration. You get production-ready code in minutes instead of weeks.

Can I convert Figma designs to code?

Yes! CodeFlow has advanced Figma-to-code conversion supporting both React and Flutter. Import your Figma designs and get production-ready components with proper theming, responsive layouts, and 10/10 code quality.

What programming languages and frameworks do you support?

CodeFlow specializes in React (with Vite and TypeScript) for web applications and Flutter (with Material Design 3) for mobile apps. Backend code uses Express.js with TypeScript and PostgreSQL databases.

How much does CodeFlow cost?

CodeFlow offers flexible pricing: Free (1,000 credits/month), Starter ($29/month, 10,000 credits), Professional ($99/month, 50,000 credits), and Organizations (from $499/month, custom credits). Use credits only for what you generate — no hidden fees.

We Stopped Shipping AI-Generated Code That Hasn't Been Verified

Watch the Demo

The Problem With "Generate and Hope"

Most design-to-code tools hand you the output and walk away.

You get a bundle of components. Maybe they compile. Maybe the types are right. Maybe the layout holds together. You won't know until you open the code, run it, and start debugging.

That's not a workflow. That's a lottery.

What We Built

We've shipped a Self-Correcting Generation Loop — and it changes how every Figma-to-code conversion works inside CodeFlow Lab.

Here's what happens now, automatically, on every conversion:

Generate — Figma design is parsed through our IR pipeline and compiled into React, Vue, or Flutter code

Validate — The output is checked using ts-morph for TypeScript correctness, structural integrity, and semantic quality

Fix — If issues are found, the system diagnoses the errors and applies targeted fixes — no human in the loop

Re-validate — The corrected code is checked again, up to 3 iterations, until it passes or reaches maximum quality

Score — A final quality score (0–10) is assigned and persisted alongside the project

All of this happens before the code reaches you.

No Developer Needed

The loop runs autonomously using CodeFlow Lab's agentic framework. It doesn't ask for help. It doesn't surface half-broken output. It either fixes the issue itself or tells you exactly what couldn't be resolved and why.

Every iteration is recorded — what errors were found, what the AI's reasoning was, what changed in the code, and how the score improved. This gives us clear before-and-after evidence of code quality improvement.

The Self-Correction Workspace

To make this visible, we've added a Self-Correction tab in the code editor.

Left Panel: AI Thinking & Score Timeline

Step-by-step iteration timeline

Error counts per iteration

AI reasoning for each fix

Quality score arc from initial to final (0–10 scale)

Right Panel: Code Diff View

LCS-based unified and split diff views

Per-iteration before/after code snapshots

Clear visualization of exactly what changed and why

Clean Compilation State

Even when everything compiles perfectly on the first try, the tab still appears — with a green checkmark and a "Clean Compilation" message. This confirms the code was fully validated, not just generated.

This matters for trust. Users and stakeholders can see that every output went through the verification pipeline, whether it needed fixes or not.

Why This Matters

AI code generation without verification is just text prediction with extra steps.

The self-correcting loop turns CodeFlow Lab from a generator into a generator that proves its own output. That's the difference between a demo and a production tool.

For developers: Less time debugging generated code. The output you receive has already been through the cycle you'd run manually.

For teams: Auditable quality history. Every project carries its verification record.

For stakeholders: Visible proof that AI-generated code meets a measurable quality bar.

What's Next

This is the foundation for deeper quality enforcement — design-token compliance checks, accessibility scoring per component, and cross-framework parity validation.

The goal isn't perfection on every generation. It's convergence — getting reliably closer to production-ready with every iteration, and being transparent about the journey.

marcello.cultrera@code-flow-lab.com

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We Stopped Shipping AI-Generated Code That Hasn't Been Verified. Here's How.