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How We Brought AI Into a React Web App — And Why It Changed Everything

2 min read
React AI

AI isn’t just hype. When integrated properly, it becomes a powerful extension of your product, your workflows, and your user experience. In this case study, we’ll walk through how we added AI into an existing React project — without rewriting the architecture or adding unnecessary complexity.

The Challenge

Our client had a modern React-based web application serving B2B customers. The product was strong — but the team wanted to:

  • Reduce manual admin for their users
  • Improve onboarding without hiring support staff
  • Add intelligent recommendations based on usage

The biggest constraint? They didn’t want to rebuild anything. Speed and seamless integration were key.

Our Approach

Rather than retrofitting a chatbot or plugging in something generic, we started with the workflow — not the tech. Our goal: identify where AI could actually solve a real problem without overwhelming the stack or user.

Step 1: Identify AI-worthy Use Cases

We started with user behavior analytics and feedback. Three use cases stood out:

  • Smart form assistance: guide users through long data input steps
  • Auto-generated summaries of dashboard reports
  • Natural language Q&A on platform features

These were pain points where automation could enhance, not distract.

Step 2: Add AI Logic Without Bloating the Frontend

We kept the frontend lean by using:

  • fetch() and axios to call backend routes securely
  • debounce and async/await for optimal UX timing
  • Suspense boundaries for loading fallback handling

On the backend, we created middleware with Laravel (could be Node as well) that handled:

  • Prompt engineering per use case
  • Authentication and rate-limiting
  • Context injection (user, plan, past usage)

We routed requests to OpenAI and Anthropic based on function — OpenAI for summaries, Claude for broader answers.

Step 3: Keep Control With Custom Prompts

We didn’t use base LLMs directly. Instead, we created our own prompt templates using:

  • Internal terminology
  • Guardrails to avoid hallucinations
  • Data-specific context pulled from the app database

Each prompt was dynamically assembled server-side. This kept the agent both smart and safe.

The Result

  • Users were 42% faster filling out long forms
  • 68% of dashboard users clicked on AI-suggested summaries
  • Support volume dropped 29% in the first month — without launching a single FAQ page

The client didn’t just get AI — they got intelligent UX improvements that users actually appreciated.

Lessons Learned

Here’s what we’d recommend for any team considering AI in React apps:

  • Don’t let the model lead the product. Start with use cases
  • Keep the frontend light. Route logic through middleware
  • Don’t trust generic prompts — contextualize everything
  • Add metrics. Know what “success” looks like

Want to Add AI to Your React App?

We help companies implement AI where it actually improves performance — not just as a gimmick.

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→ Or contact us to plan your integration

Because real AI doesn’t just feel smart — it performs.