Figma to Production Code: How We Turn Designs Into Live React Apps
TL;DR
AI can generate about 70% of a React app from a Figma file. The remaining 30% — state handling, error and empty states, accessibility, and performance — is where production apps are won or lost. This is the six-step Figma to production code workflow we use on client projects, including design tokens and Code Connect.
Figma to Production Code: How We Turn Designs Into Live React Apps
Turning Figma to production code is still one of the most painful steps in building a product. Figma's own research found that 92% of designers and developers agree the handoff needs improvement. That number has barely moved in years, even as the tools got better.
The reason is simple. A design file and a codebase are two different things. A Figma frame describes how something looks at one size. A React app has to handle every size, every state, every error, and every user. Closing that gap is engineering work, not export work.
At Craxinno, we have shipped this handoff across 210+ projects. This post explains exactly how we do it: the preparation that makes designs convertible, the tooling we use, the part AI handles well, and the part that still needs a developer. If you are evaluating how to get your designs into a live app, this is the honest version.
Why most Figma to React conversions fail
Before the process, it helps to know where teams go wrong. Three failures show up again and again.
The design was never built for code. A designer creates a beautiful screen using absolute positioning and one-off colors. It looks perfect at one width. In code, it breaks on mobile, because nothing tells the layout how to flex. The fix belongs in Figma, not in the codebase.
Every value is hard-coded. The design uses 47 slightly different hex values and a dozen padding numbers. The developer copies each one by hand. Six months later the brand changes, and someone has to hunt down every instance. This is the single biggest source of long-term drift.
The export was treated as the finish line. A tool generates a React component that looks right in a screenshot. Nobody checks keyboard access, loading states, or what happens when the API returns nothing. The app looks done and behaves broken.
Our Figma to production code workflow, step by step
Here is the process we run on client work. It is deliberately front-loaded, because the preparation is what makes the rest fast.
Step 1: Audit the design file before writing any code
We open the Figma file and check three things. Is auto layout used on every frame that needs to be responsive? Are components named the way they will exist in code? Are colors, type, and spacing defined as styles and variables rather than one-off values?
If the answer is no, we fix that first. This step feels like a delay. It is the opposite. A file that is properly structured converts in a fraction of the time, and the code that comes out is maintainable.
Step 2: Extract design tokens as the single source of truth
We pull colors, spacing, typography, and radii out of Figma as design tokens, then map them into the codebase as CSS variables or Tailwind theme keys.
This is the highest-leverage step in the entire workflow. Once every value in the code traces back to a named token, a rebrand becomes a single change in one file instead of a search across fifty components. It also removes ambiguity between teams. When a designer says primary-600 with spacing-4, the developer knows exactly what that means. No screenshots, no back-and-forth.
Step 3: Map Figma components to React components one-to-one
A button in Figma with size and variant properties should become a React Button component with size and variant props. Same name, same structure, same options.
We use Figma Dev Mode and Code Connect to make that mapping explicit. The payoff is that the design system and the codebase stop drifting apart. A change in one has an obvious home in the other.
Step 4: Generate the first pass with AI, then engineer the rest
This is where we are direct about what AI does and does not do. Modern tooling, including Figma's MCP server and AI coding assistants, can read component properties, tokens, and auto layout directly from the file and produce a solid first draft. Claude and Claude Code are part of our daily workflow here, and they genuinely compress the tedious part.
But the first pass is roughly 70% of the job. It gets structure and styling close. It does not reliably handle state management, error boundaries, loading and empty states, data fetching, or accessibility. Research cited across the industry found automated accessibility tools catch only about 30% of WCAG issues in generated output. Engineering cleanup is not optional. It is the work.
Step 5: Build the states the design never showed
Every design shows the happy path. Production needs the rest. For each screen we build the loading state, the empty state, the error state, and the partial-data state.
This is the step that separates a demo from a product, and it is the one most conversion tools skip entirely because a Figma file simply does not contain that information.
Step 6: Test responsiveness, accessibility, and performance
We check the app at real breakpoints, not just the two the design showed. We verify keyboard navigation, focus order, colour contrast, and screen reader labels. We measure bundle size and load performance.
Then we ship it, usually on Next.js and Vercel, with the component library documented so the next developer inherits something clean.
The stack we use for Figma to React work
Our production stack is deliberately standard, because standard means maintainable by whoever comes next.
For the design side: Figma with Dev Mode, Code Connect for component mapping, and design tokens exported as the source of truth.
For the code side: React and Next.js with TypeScript, Tailwind for styling driven by those tokens, and a documented component library.
For the AI-assisted layer: Claude and Claude Code for first-pass generation and refactoring, always followed by human review.
There is no exotic lock-in here. If you take the codebase elsewhere, any competent React team can pick it up. That is intentional.
When to use a conversion tool, and when not to
Honest guidance, since this comes up on nearly every project.
Use a one-shot conversion tool when you need a marketing page, a landing page, an internal tool, or a throwaway prototype. Tools like Anima, Locofy, and Visual Copilot get you to a working build quickly, and the output is good enough for something you will not maintain for years.
Do not architect a product on them. For an application you will grow, hire on, and maintain, generated output tends to produce code that works but is unpleasant to extend. The cost shows up later, in every feature that takes longer than it should.
The rule we use: if the code will live longer than six months or be touched by more than two developers, engineer it properly.
What good Figma to code handoff actually delivers
When the workflow above is done well, the outcomes are concrete.
Rebrands take hours instead of weeks, because tokens live in one place. New screens ship faster, because the component library already exists. Visual bugs get rarer, because values are named rather than copied. And designers and developers stop arguing about padding, because they share a vocabulary.
That is the real return on the handoff. Not pixel accuracy on day one, but speed on day ninety.
Work with a team that ships design to production
If you have Figma designs and need them live as a fast, accessible, maintainable React app, the Craxinno team does this work daily. See recent builds in the Craxinno portfolio, view our full capabilities on the services page, or email hello@craxinno.com.
Frequently Asked Questions
Can AI convert Figma designs to production React code?+
AI can generate a strong first pass, typically around 70% of the work. Tools using Figma's MCP server read component properties, design tokens, and auto layout directly from the file. But generated code rarely handles state management, error and empty states, data fetching, or accessibility properly. Automated accessibility tools catch only about 30% of WCAG issues, so engineering review is still required.
What are design tokens, and why do they matter for Figma to code?+
Design tokens are named values for colors, spacing, typography, and radii, shared between the design file and the codebase. They matter because they remove hard-coded values. When every value traces back to a token, a rebrand is one change in one file instead of a search across dozens of components.
How do you prepare a Figma file for code conversion?+
Use auto layout on every frame that needs to be responsive, since absolute positioning converts to fixed pixels and breaks on mobile. Name components the way they will exist in code, so a frame becomes a readable component. Define colors, type, and spacing as styles and variables rather than one-off values.
Should I use a Figma to React conversion tool?+
Use one for marketing pages, landing pages, internal tools, and prototypes, where speed matters more than long-term maintainability. Avoid architecting a real product on generated output. A good rule: if the code will live longer than six months or be touched by more than two developers, engineer it properly.
How long does Figma to production code take?+
It depends mostly on how well the design file is prepared. A well-structured file with auto layout, named components, and defined styles converts far faster than an unstructured one. A single marketing page can ship in days. A full application with a component library, states, and accessibility work typically takes weeks.
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AWSAWS S3 Backup: Complete Setup Guide (2026)
AWS S3 backup can mean three different things, and picking the wrong one is why so many backup setups quietly fail. This guide walks through all three methods, when to use each, and the exact steps to set them up, so your data is actually protected, not just assumed to be. Here is the short version before the detail. S3 versioning protects against accidental overwrites and deletes inside one bucket. AWS Backup gives you scheduled, centralized, point-in-time backups you can restore from. Cross-region replication copies your data to another region for disaster recovery. Most solid setups use versioning as the foundation, then add AWS Backup or replication on top. This guide sets up all three. A quick note before you start: run every command in this guide against a test bucket first, never a production bucket, until you are confident in the result. The three ways to back up S3, and when to use each Most confusion around S3 backup comes from treating these as one thing. They are not. Here is what each does. S3 versioning keeps every version of an object. Overwrite a file, and the old version is still there. Delete one, and it is recoverable. Think of it as an undo button for a single bucket. It is the foundation of almost every backup strategy, and it is required for the other methods. AWS Backup is a managed service that takes scheduled, point-in-time backups of your bucket and stores them in a backup vault you can restore from. It is the closest thing to traditional, centralized backup, with policies, retention, and cross-account support. Cross-region replication automatically copies objects to a bucket in another AWS region. If an entire region has an outage, your data still exists elsewhere. This is disaster recovery, not day-to-day backup. The honest rule: enable versioning first, always. Then add AWS Backup for scheduled restore points, and cross-region replication if you need disaster recovery. Now let us set each one up. Before you start: prerequisites Get these in place first. A missing prerequisite is the most common reason a backup setup fails silently. An AWS account with billing enabled and an S3 bucket you can test on. Do not run your first attempt against production. An IAM user or role with S3 read and write permissions on the target bucket, including permission to manage versioning and lifecycle configuration. AWS CLI v2, the latest version, configured with your credentials using the aws configure command. A rough idea of your retention needs: how many versions you want to keep, and for how long. Method 1: Enable S3 versioning (the foundation) Versioning is where every backup strategy starts. When enabled, S3 keeps every version of an object, so an accidental overwrite or delete is always recoverable. Using the AWS Console Sign in to the AWS Management Console and open the S3 service. In the navigation pane, click Buckets, then select the bucket you want to protect. Open the Properties tab, find Bucket Versioning, click Edit, choose Enable, and save. Versioning is now on for that bucket. Using the AWS CLI To enable versioning from the command line, run this, replacing the bucket name with your own: aws s3api put-bucket-versioning --bucket your-bucket-name --versioning-configuration Status=Enabled To confirm versioning is active: aws s3api get-bucket-versioning --bucket your-bucket-name One important caveat: versioning keeps every version forever unless you tell it not to. Without a cleanup rule, your storage costs grow indefinitely. That is what the next step fixes. Method 2: Add a lifecycle policy to control cost Versioning alone will pile up old versions and inflate your bill. A lifecycle policy automatically manages those old versions, moving them to cheaper storage or deleting them after a set time. A common, sensible rule: keep noncurrent (older) versions for 30 days, then delete them. That gives you a month to recover a mistake without paying to store every version forever. Create a file named lifecycle-policy.json with your rule, then apply it: aws s3api put-bucket-lifecycle-configuration --bucket your-bucket-name --lifecycle-configuration file://lifecycle-policy.json Adding a lifecycle rule to a versioned bucket is an AWS best practice. It prevents old versions from accumulating, which both controls cost and keeps request performance fast. Method 3: Set up AWS Backup for scheduled, restorable backups Versioning protects within a bucket. AWS Backup gives you true , centralized, point-in-time backups you can restore from a vault, the closest thing to traditional backup software. Two requirements before you begin. First, versioning must be enabled on the bucket; AWS Backup requires it. Second, the role you use needs the AWS managed policies for S3 backup and restore attached. The setup, step by step Open the AWS Backup console. Create a backup vault, which is the secure store for your backups. Create a backup plan, where you set the schedule (for example, daily) and how long to keep each backup. Assign your S3 bucket to the plan using its resource ID or a tag. AWS Backup now takes backups automatically on your schedule. To restore, you pick a recovery point from the list, which represents your bucket's state at that moment, and restore the whole bucket or specific prefixes to the original bucket, another bucket, or a new one in the same region. One cost note: AWS Backup stores all versions present when the backup runs, including objects scheduled for deletion. Setting a lifecycle expiration on your versions, as in Method 2, keeps those backup costs down. Method 4 (optional): Cross-region replication for disaster recovery If you need protection against an entire region failing, replicate to another region. This is disaster recovery, and it is optional for most teams but essential for critical data. Both the source and destination buckets must have versioning enabled. Create the destination bucket in a different region: aws s3 mb s3://your-backup-bucket-dr --region us-west-2 Enable versioning on it: aws s3api put-bucket-versioning --bucket your-backup-bucket-dr --region us-west-2 --versioning-configuration Status=Enabled Then configure a replication rule on the source bucket (via the console's Management tab or the CLI) pointing to the destination. New objects will replicate automatically. Which method should you actually use? Here is the honest guidance for common situations. For a small project or side app: enable versioning plus a lifecycle policy. That alone protects you from the most common disaster, accidental deletion, at almost no cost. For a business application: versioning plus a lifecycle policy plus AWS Backup. You get accidental-delete protection and scheduled, restorable, point-in-time backups. For critical or regulated data: all of it, versioning, lifecycle, AWS Backup, and cross-region replication, so you are covered against everything from a fat-fingered delete to a full region outage. The mistake to avoid: assuming S3's famous durability means your data is backed up. S3 is extremely durable against hardware failure, but durability does not protect you from someone deleting the wrong thing or an app writing bad data. That is what backups are for, and why versioning should always be on. Setting up cloud infrastructure the right way A backup strategy is one piece of getting cloud infrastructure right. If you are building an application that needs reliable, secure, well-architected AWS setup, from storage to deployment, the Craxinno team builds and maintains production cloud infrastructure for clients worldwide. See recent work in the Craxinno portfolio , view our full stack on the technologies page , or email sales@craxinno.com .
AI AgentsAI Agent Development Company: How to Choose the Right One
AI Agent Development Company: How to Choose the Right One Choosing an AI agent development company comes down to one test: can they show you a working agent in production, or only a slide deck? Most firms now market "agentic AI," but a large share are wrapping a simple API and calling it an agent. The difference between those two is the difference between a project that ships and one that quietly fails after six months. This guide gives you a practical way to tell them apart. You will learn the exact questions to ask, the warning signs to walk away from, what the engagement should cost, and how to shortlist an AI agent development company that can actually deliver an autonomous system, not a demo. The quick answer: what to look for The right AI agent development company can do five things. It can show you a real agent running in production. It has a clear reason for its choice of orchestration framework. It can explain how it handles agent failures. It has a real observability setup. And it will propose an architecture before you sign, not just a timeline. If a company does all five, it belongs on your shortlist. If it cannot do most of them, keep looking, no matter how good the pitch sounds. The rest of this guide explains each test and why it matters. First, what an AI agent development company actually does A quick definition, because the term is used loosely. An AI agent development company builds software that pursues goals on its own, not just chatbots that answer questions. A real agent plans multi-step tasks, connects to your systems, takes actions, and recovers when a step fails. That is a harder job than building a chatbot, and it needs different skills: orchestration, systems integration, failure handling, and observability. Many firms that list "AI agents" on their services page have built chatbots, not agents. Knowing the difference is the first step to choosing well. For the deeper distinction, see our guide on AI agents vs chatbots . The five questions that reveal the real ones Ask these five questions on your first call. The answers sort a shortlist faster than any proposal. 1. Can you show me a production agent, not a sandbox demo? This is the single most important question. A company with real experience can name a working agent, describe the workflow it owns, and explain what happens when it fails. A company without one will show a capabilities deck and talk in generalities. Ask for a specific, live example. Vagueness here is disqualifying. 2. What orchestration framework do you use, and why? Building agents means choosing tools like LangGraph, AutoGen, CrewAI, or Model Context Protocol, and each involves real trade-offs. A strong company has made a deliberate choice and can explain the reasoning. A company that has not heard of these, or cannot explain its choice, is building on guesswork. 3. How do you handle agent failures? Agents break in four main ways: hallucination, prompt injection, a step failing mid-task, and getting stuck in loops. A serious company names specific ways it handles each. A weak one waves the question away, which means you will be the project where they learn these lessons. 4. What does your observability setup look like? An agent you cannot observe is one you cannot debug. A mature company tracks what its agents do step by step, monitors errors per tool, and can trace a task from start to finish. A vague answer, like "we check the logs," signals a team that has not run agents in production. 5. Will you propose an architecture before we start? A company with real expertise asks sharp questions, identifies edge cases, and proposes a specific approach with trade-offs before the engagement begins. A company without it sends a timeline and a price. The first is engineering. The second is order-taking. The warning signs to walk away from Some signals tell you to keep looking, often before you even reach the questions above. Only demos, no production. If a company can only show sandbox demos or internal experiments, you would be paying for their first real deployment. That is an expensive place to be. No opinion on frameworks or failure. A team that cannot discuss orchestration trade-offs or failure handling has not shipped agents at scale, whatever the website says. Vague pricing. Established teams can scope a range within a day or two. A company that will not give a range, or only quotes open-ended hourly work, is signaling weak project discipline. Overpromised timelines. Any company that promises a production agent in two weeks, without seeing your data or systems, is either guessing or has never shipped one. A huge service list, a tiny team. A small team claiming deep expertise in agents, RAG, computer vision, voice AI, and MLOps all at once usually has one person stretched across each. Ask how many engineers actually build agents. What matters more than the model: integration Here is the thing most buyers miss. The hardest part of an AI agent is rarely the language model. It is the integration, the connections to your CRM, your database, your payment system, all the places the agent has to act. An agent is only as reliable as the weakest link in that chain of systems. So when you evaluate an AI agent development company, weigh its integration and engineering discipline more heavily than its enthusiasm about models. A team that talks endlessly about which model it uses, but vaguely about how it connects to your systems, has the emphasis backwards. What hiring an AI agent development company costs Cost depends on how much the agent must do, but here are realistic 2026 bands, based on rates common to established teams in India, which run well below US and UK firms. A proof of concept runs $10,000 to $30,000. A single workflow, built to prove the agent works. A production agent runs $25,000 to $75,000. One well-scoped autonomous workflow with real integrations, error handling, and monitoring. A multi-agent enterprise system runs $75,000 and up. Multiple agents, many integrations, human checkpoints, and full observability. A realistic timeline for a production agent is three to six months. Budget separately for model usage, which scales with how much the agent works. For the full breakdown, see our guide on the cost to build an AI agent . How to run the selection process A simple process gets you to the right company without wasted months. Scope the workflow first, not the technology. Start with one specific, measurable process you want automated, with clear inputs and a clear definition of done. This makes every conversation with a vendor sharper. Shortlist on the five questions. Use the questions above to cut a long list to two or three companies that can actually answer them. Ask for a paid pilot. A strong company will happily prove itself on a small, paid first task before a large commitment. This removes your risk and reveals how they really work. Check domain fit. If your agent operates in a regulated field like finance or healthcare, favor a company that has handled the compliance and failure consequences specific to that space. To see the range of what agents do across industries, see our guide on practical AI agent use cases. The best AI agent development company for you is not the one with the flashiest pitch. It is the one that can show real work, explain its choices, and prove itself on a small task first. Choose a partner that ships, not one that demos The right AI agent development company depends on your workflow, your systems, and your industry. There is no universal best, only the right fit for your build, proven on real work rather than promised in a deck. The Craxinno team builds production AI agents and is happy to review your workflow, propose an architecture, and prove the approach on a scoped first task. See recent AI work in the Craxinno portfolio , view our full stack on the technologies page, or email sales@craxinno.com .
AI AgentsAI Agents vs Chatbots: Which One Should Your Business Build?
AI agents vs chatbots comes down to one difference: a chatbot answers, an agent acts. A chatbot responds to a question and stops. An AI agent takes a goal, plans the steps, works across your systems, and completes the task on its own. Choosing between them is really a choice about how much work you want the software to actually do. Here is the honest starting point. Most businesses do not need the more advanced option for every job. A chatbot is cheaper, faster to build, and perfect for answering questions. An AI agent costs more and takes longer, but it can finish tasks a chatbot only talks about. The right choice depends on whether your problem is answering questions or completing work. This guide explains the real difference, when each one wins, what each costs, and how to decide which your business should build. The quick answer Build a chatbot if your goal is to answer questions, guide users to information, or handle simple, repetitive conversations. It is cheaper, faster, and enough for most FAQ and support-deflection needs. Build an AI agent if your goal is to complete tasks, not just answer, such as resolving a support ticket end to end, processing an order, or working across several systems. It costs more but does far more. Start with a chatbot and grow into an agent if you are early, testing, or unsure. Many successful agents began as chatbots that proved the need before the bigger investment. What is a chatbot? A chatbot is software that has a conversation with a user. It takes a message and returns a response. Modern chatbots, powered by language models, can hold natural conversations, answer questions from a set of documents, and guide people to the right information. But a chatbot has a hard limit. It responds, and then it waits. It does not take action on its own. Ask a chatbot to "track my order," and a good one tells you how to check your order status. It does not go and check for you. It is a conversation tool, and within that job it is excellent, fast, and inexpensive. What is an AI agent? An AI agent is software that pursues a goal. You give it an objective, and it plans the steps, uses tools and systems, makes decisions, and works until the task is done. The difference is action. Ask an AI agent to "track my order," and it looks up your order in the system, checks the real-time shipping status, tells you where it is, and, if it is late, offers a refund or a reship, all on its own. It kept memory across steps, used external systems, and completed the task, not just described it. That is the line between the two: a chatbot answers, an agent acts. The core difference, side by side Put plainly, four things separate them. Action. A chatbot responds with information. An agent takes action across systems to complete a task. Memory. A chatbot usually handles one exchange at a time. An agent keeps context across many steps, remembering what it has already done. Autonomy. A chatbot waits for the next message. An agent works on its own, making decisions until the goal is reached. Tools. A chatbot mostly talks. An agent connects to your CRM, your database, your payment system, and acts inside them. A simple way to remember it: if the job is to answer, you want a chatbot. If the job is to do, you want an agent. When your business should build a chatbot A chatbot is the right choice more often than founders expect. Choose one when these apply. Your main need is answering questions. FAQ, product information, policy questions, and basic support are exactly what chatbots do well. You want to deflect support tickets. If most of your support volume is repetitive questions, a chatbot can handle a large share and free your team, at a fraction of the cost of an agent. You are on a tight budget or timeline. Chatbots are cheaper and faster to build, so they are the pragmatic first step for many businesses. You are testing an idea. If you are not yet sure how much automation you need, a chatbot proves the value before you invest in an agent. When your business should build an AI agent Choose an agent when answering is not enough and the job needs to get done. You need tasks completed, not just answered. Resolving a refund, processing an order, updating records, booking an appointment, an agent finishes these; a chatbot only explains them. Your workflow crosses several systems. If completing a task means touching your CRM, your inventory, and your payment system, an agent works across all of them; a chatbot cannot. Your support is drowning in resolvable tickets. When the volume is high and the tasks are real work, not just questions, an agent resolves them end to end, which is where the biggest returns show up. For real examples, see our guide on practical AI agent use cases for businesses . You want automation that pays back at scale. Agents cost more upfront but replace far more manual work, so at volume the economics favor them. What each one costs The cost gap is real and it reflects the capability gap. A chatbot is cheaper. A capable, document-aware chatbot is a relatively contained build, because it does one thing: converse and answer. Most businesses can launch one quickly and affordably. An AI agent costs more. An agent needs planning logic, tool integrations, error handling, memory, and safety checks, all the machinery that lets it act, not just answer. That is real engineering, and the price reflects it. For a full breakdown, see our guide on the cost to build an AI agent . The honest way to think about it: do not pay for an agent to do a chatbot's job. If answering questions solves your problem, a chatbot is the smarter spend. Pay for an agent only when completing tasks is the actual goal. The smart path: start simple, grow into an agent Here is the pattern that works for most businesses, and it avoids overspending. Start with a chatbot to handle the questions. Prove that automation helps, learn where users get stuck, and see exactly which tasks they wish the software could finish. Then, once you know the specific workflows worth automating, build an agent for those, and only those. This sequence keeps early costs low and makes the eventual agent far better, because it is built around real user behavior instead of guesses. Building a full agent before you understand your own workflow is how businesses overspend on automation nobody asked for. The same scope discipline that keeps any software project on budget applies here: prove the small thing first, then expand. So, which should your business build? Neither is better in the abstract. The right choice depends on your goal. If you need to answer questions, build a chatbot. It is cheaper, faster, and enough. If you need to complete tasks across systems, build an AI agent, because a chatbot will only ever describe the work an agent actually does. And if you are unsure, start with a chatbot, learn, and grow into an agent when a real workflow demands it. The most expensive automation is the kind built for the wrong job. Match the tool to the goal, and start smaller than you think. Ready to build the right one? The right choice between an AI agent and a chatbot depends on your goals, your systems, and your budget. There is no universal answer, only the right fit for your business. The Craxinno team builds both chatbots and production AI agents , so we can help you choose honestly, including when a simple chatbot is all you need. See recent AI work in the Craxinno portfolio, view our full stack on the technologies page, or email sales@craxinno.com .
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