You will get a custom AI feature built into your existing web or mobile app
Top Rated
Top Rated
Project details
I'll add a production-ready AI feature to your existing web or mobile app — AI content generation, smart classification, summarization, a copilot, or a natural-language assistant — wired cleanly into your current stack.
What you get:
• One or more AI features built into your app (not a separate tool)
• Clean integration with your codebase via OpenAI, Claude, or Gemini APIs
• Polished UX: streaming responses, loading and error states, sensible guardrails
• Source code, brief docs, and deployment to your environment
Works with React, Next.js, Node, Python, React Native, and most modern stacks.
I'm a Top Rated AI engineer with a 100% Job Success Score who ships LLM features into real products. Tell me what you want the feature to do and I'll recommend the right model, approach, and tier.
What you get:
• One or more AI features built into your app (not a separate tool)
• Clean integration with your codebase via OpenAI, Claude, or Gemini APIs
• Polished UX: streaming responses, loading and error states, sensible guardrails
• Source code, brief docs, and deployment to your environment
Works with React, Next.js, Node, Python, React Native, and most modern stacks.
I'm a Top Rated AI engineer with a 100% Job Success Score who ships LLM features into real products. Tell me what you want the feature to do and I'll recommend the right model, approach, and tier.
AI Algorithms
Large Language Model, Multimodal Large Language ModelAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Models
AlphaCode, ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$400
|
Standard
$1,100
|
Advanced
$2,600
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 24 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | - | - | |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
Frequently asked questions
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About Muhammad
AI Engineer | LLM Apps, RAG Chatbots & AI Features for SaaS
100%
Job Success
Lahore, Pakistan - 8:08 am local time
What I can help you with:
• Add AI to an existing product: chatbots, copilots, semantic/document search (RAG), content automation
• Build an AI SaaS feature end to end — mobile or web front end, API backend, and the LLM layer
• Turn an AI prototype into something stable, secure, and production-ready
Recent results:
• Took a half-built financial SaaS (React, Supabase) to a working, demoable product on a tight deadline (5.0 stars)
• Built a RAG-based AI finance app that auto-categorizes transactions and answers questions over user data
• Shipped GPT-4 content moderation + chat for a community platform
Core stack:
• AI/LLM: OpenAI, Claude, Gemini, LangChain, LlamaIndex, RAG, embeddings, pgvector/Pinecone
• Backend: Node.js, Python (FastAPI/Flask), Laravel, PostgreSQL, Redis, Docker
• Frontend/Mobile: React, Next.js, TypeScript, React Native
How I work: clear scope up front, small reviewable increments, and short regular updates so you're never guessing. Async-friendly and usually responsive within a few hours.
Want to add AI to your product or build a feature from scratch? Send a message with what you're trying to build and I'll reply with a clear plan and next steps.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Scope & integration plan
Confirm the feature behavior, review your codebase and stack, and plan the cleanest way to integrate.
Build the AI feature
Implement the feature against the right model/API with prompts, streaming, and error handling.