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// OpenAI · Claude · Gemini → Laravel · WordPress · Flutter

Your app,
now with
AI built in.

I connect OpenAI, Claude, and Gemini APIs to the software you've already built — adding AI chat, document analysis, smart search, and workflow automation without a rebuild. 17+ years of full-stack development, now applied to AI integration.

✓ Top Rated Upwork
✓ 100% Job Success
✓ 17+ Years Dev
Models I integrate
🤖
OpenAI GPT-4o
Most versatile — chat, vision, function calling
Most used
🔮
Anthropic Claude
200K context — best for large document analysis
Long docs
💎
Google Gemini
Multimodal — text, image, audio, video
Multimodal
⚡
Open Source (Llama, Mistral)
Self-hosted — no API costs, full data privacy
Private
178%
YoY growth in AI integration demand on Upwork 2026
12+
Years full-stack development to support AI builds
4
AI models integrated — OpenAI, Claude, Gemini, Llama
100%
Job Success Score on Upwork
Use Cases

What Gets Built When AI Meets Your App

Not generic AI features — specific capabilities that slot into your existing product and solve a real problem your users have right now.

AI chatbot integrated into a web application for customer support

AI Chatbot & Support Assistant

A conversational AI embedded in your Laravel or WordPress app — trained on your documentation, product data, or knowledge base. Answers questions, routes tickets, and handles customer support at any hour.

GPT-4oClaudeStreamingContext
AI document analysis and contract review automation

Document Analysis & Extraction

Upload a contract, invoice, medical record, or report — AI extracts key data, flags clauses, summarises content, and outputs structured JSON your system can act on. Claude's 200K context handles huge documents.

ClaudePDF ParseExtraction
AI content generation for CMS and marketing automation

AI Content Generation

Automated blog posts, product descriptions, email sequences, or social content — generated on your brand's tone, triggered by your CMS or CRM workflow, and reviewed in a dashboard before publishing.

GPT-4oWordPressBrand Voice
AI smart search and semantic search for web applications

Smart Search (Semantic)

Replace keyword-dependent search with AI-powered semantic search — users find what they mean, not just what they typed. Built with embeddings and vector similarity on your existing database.

EmbeddingsVector SearchLaravel
AI workflow automation and business process intelligence

AI Workflow Automation

AI that reads incoming emails, tickets, or form submissions — classifies, routes, drafts replies, and triggers downstream actions in your CRM or backend. Eliminates manual triage at scale.

n8nLaravelCRM
AI features integrated into Flutter mobile iOS Android app

AI in Mobile Apps (Flutter)

AI chat, image analysis, voice transcription (Whisper), and smart recommendations embedded directly in your Flutter iOS/Android app — via a Laravel backend that handles the API calls securely.

FlutterWhisperGPT-4o Vision
Where It Connects

AI Into Your Existing Stack

I don't build a separate AI product — I wire the AI model into whatever you've already built, so users get AI capabilities inside the product they already use.

🔺
Laravel
Service layer + streaming API
🌐
WordPress
Custom plugin + REST endpoint
⚛️
React / Next.js
Streaming UI + server actions
📱
Flutter
Via secure Laravel API proxy
🟠
HubSpot / CRM
AI-enriched lead data
⚡
n8n / Make
AI nodes in automation flows
📄
Your Database
Context injection + retrieval
💬
WhatsApp / Slack
AI agent on any channel
Have an existing app that could use AI built in?
How It's Built

Three Things Done Right on Every AI Integration

Most developers treat AI integration as "add a fetch() call to the OpenAI API." Production AI integration has three more layers that most tutorials skip.

Server-side AI API key management keeping secrets secure
Security

API keys never leave the server.

The single most common mistake in AI integration: calling the OpenAI API directly from the browser, exposing your API key in client-side JavaScript. Anyone can open DevTools and steal it.

  • All AI API calls go through your Laravel backend — never the browser
  • API keys stored in server environment variables, never in code
  • Rate limiting per user to prevent abuse and runaway costs
  • Request logging for cost monitoring and debugging
Context management and conversation history in AI chat integration
Context

The AI knows your data, not just the prompt.

A generic AI chatbot answers from its training data. A properly integrated one answers from YOUR data — your products, your docs, your customer's history. That's the difference between a toy and a tool.

  • System prompts that encode your business context, tone, and rules
  • Conversation history managed in your database — AI remembers the session
  • Relevant data retrieved from your database and injected per request
  • Document context sent with the prompt — AI reads what the user uploaded
AI cost monitoring token usage tracking and response caching
Cost Control

Token costs monitored before they surprise you.

AI API costs scale with usage. Without monitoring, a surge in users or a single runaway prompt can generate a $500 bill overnight. I build cost control in from day one.

  • Token usage logged per request, per user, per feature
  • Response caching for repeated queries — same question, no API call
  • Per-user and per-day spend caps with graceful error handling
  • Model selection optimised — GPT-4o-mini for simple tasks, GPT-4o for complex
Why Mehdi

AI Integration by a Full-Stack Developer — Not a Prompt Engineer

The AI part is 20% of the work. The other 80% is the Laravel service layer, the database design, the API security, the streaming UI, the cost monitoring, and the error handling. That's where 17 years of development experience matters.

🔒

Security-first implementation

API keys server-side, rate limiting per user, spend caps, and request logging from day one — not retrofitted after a surprise bill arrives.

🏗️

Builds on your existing codebase

I integrate AI into what you've already built — I don't suggest rebuilding it. Your Laravel app, WordPress site, or Flutter app gets AI capabilities without a migration.

🤖

Model-agnostic experience

OpenAI, Claude, Gemini, and open-source Llama models — I work with all four and recommend the right one for each use case rather than defaulting to one.

⭐

Top Rated on Upwork, 100% JSS

Public track record across 50+ projects — every client's actual rating on Upwork, not selected testimonials. 17 years of accountability.

💳

Full-stack means I build the UI too

The streaming chat UI, the document upload flow, the admin dashboard showing AI usage — all built by the same developer who wired the API, no handoff needed.

🌍

US, UK & AU time zones

Available for calls and reviews during your business hours — 10+ countries served without timezone friction.

FAQ

Questions About AI Integration

AI integration means connecting AI models (like OpenAI's GPT-4o, Anthropic's Claude, or Google's Gemini) to your existing application via their APIs. This adds AI capabilities — intelligent chat, document analysis, content generation, smart search, or automated decision-making — to software you've already built, without rebuilding it from scratch. The AI model provides the intelligence; your application provides the context, data, and user interface. The developer's job is building the secure bridge between them.
It depends on your use case. OpenAI's GPT-4o is the most versatile and widely supported, with the largest ecosystem. Anthropic's Claude excels at long-document analysis and following complex instructions — its 200K token context window handles very large documents that GPT-4o would need to chunk. Google's Gemini is strong for multimodal tasks (text + image + audio + video) and Google Workspace integration. For cost-sensitive applications or data privacy requirements, open-source models (Llama, Mistral) can be self-hosted. I work with all of them and recommend the right model for your specific use case.
Yes — this is the most common project I work on. For WordPress, I integrate AI via custom plugins or REST API endpoints that call OpenAI or Claude from the PHP backend, keeping your API keys server-side and never exposing them to the browser. For Laravel, I build a dedicated AI service layer that handles API calls, streaming responses, conversation history management, context injection, and cost monitoring. The AI features appear in your existing UI without a rebuild of your current codebase.
AI API costs are per-token (roughly per word) and vary by model. GPT-4o costs roughly $2.50 per million input tokens and $10 per million output tokens. Claude 3.5 Sonnet is similar. For most applications with reasonable usage, monthly API costs run $20–$200. For high-volume applications, costs can be higher. I control costs by: using cheaper models (GPT-4o-mini) for simple tasks and reserving expensive models for complex ones; caching responses to identical queries; implementing per-user daily spend limits; and logging every request so you can see exactly where costs come from and identify optimization opportunities.
RAG stands for Retrieval-Augmented Generation. Instead of asking the AI model to answer from its training data, you first retrieve relevant documents or records from your own database, then send that context alongside the user's question to the AI. This makes the AI answer from YOUR data — your product catalogue, your support docs, your customer history — rather than generic knowledge. You need RAG if you want the AI to answer questions specific to your business that weren't in the model's training data. For most AI chatbot and document analysis use cases, some form of context injection (a simpler version of RAG) is required for the AI to give useful, specific answers.
A simple AI chatbot embedded in an existing Laravel or WordPress app takes 1–2 weeks. A document analysis feature (upload → AI summary → structured extraction) takes 2–3 weeks. A full AI workflow automation (classification, routing, reply drafting, CRM sync) takes 3–6 weeks. Building from scratch with AI as a core feature takes 6–16 weeks depending on scope. I always start with a discovery call to understand your existing codebase before estimating, since the complexity of the existing system affects the integration timeline significantly.
Let's Add AI to Your App

Your existing app, now thinking.

Tell me what you're building and what AI capability you need — I'll come back within 24 hours with a clear integration plan and honest quote.

Available for new projects · AI integration specialist · Replies within 24 hours