You will get RAG architecture audit for retrieval & performance optimization
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Top Rated
Project details
A RAG system can look good in a demo and still fail when real users start asking real questions.
The problem is usually not the AI model alone. It is often the retrieval architecture. Poor chunking, weak metadata, bad search logic, noisy data, slow vector queries, missing filters, and unclear prompt structure can all lead to inaccurate answers, slow responses, and frustrated users.
This project gives you a focused RAG architecture audit to understand what is holding your system back.
I will review your Retrieval Augmented Generation setup, data flow, vector database structure, API Integration, prompt design, model usage, and response quality. The goal is to identify why your AI system is giving weak, slow, or unreliable answers and what needs to change.
You will receive practical recommendations to improve retrieval accuracy, latency, cost efficiency, and production readiness.
This is ideal for SaaS teams, AI product founders, and companies building AI assistants, knowledge base chatbots, internal search tools, or document-based AI systems.
The problem is usually not the AI model alone. It is often the retrieval architecture. Poor chunking, weak metadata, bad search logic, noisy data, slow vector queries, missing filters, and unclear prompt structure can all lead to inaccurate answers, slow responses, and frustrated users.
This project gives you a focused RAG architecture audit to understand what is holding your system back.
I will review your Retrieval Augmented Generation setup, data flow, vector database structure, API Integration, prompt design, model usage, and response quality. The goal is to identify why your AI system is giving weak, slow, or unreliable answers and what needs to change.
You will receive practical recommendations to improve retrieval accuracy, latency, cost efficiency, and production readiness.
This is ideal for SaaS teams, AI product founders, and companies building AI assistants, knowledge base chatbots, internal search tools, or document-based AI systems.
AI Algorithms
Large Language Model, Multimodal Large Language Model, YOLOAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Medical Imaging, AIOps, Conversational AI, Image Analysis, Image Processing, Image Recognition, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
GitHub Copilot, Gradio, Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, DALL-E, GPT-3, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included $700
These options are included with the project scope.
$700
- Delivery Time 5 days
- Number of Revisions 5
- AI Model Integration
- Database Integration
- Image Upscaling
- MLOps
- Model Deployment
- Model Documentation
- Model Testing & Optimization
- Model Tuning
- Natural Language Processing
- Prompt Engineering
- Setup File
- Source Code
Frequently asked questions
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Devon G.
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About Muhammad
AI Product Developer | AI Engineer | AI Agent Developer | AWS Expert
96%
Job Success
Karachi, Pakistan - 7:36 am local time
I’m an AI Engineer who builds production ready AI systems that actually work and generate revenue. I design system with AI Automation, API Integration, and scalable Cloud Architecture on AWS so your product runs reliably, not just in theory.
You don’t need a full team to build and scale your product. I handle AI development, API integration, and cloud architecture end-to-end so you move faster with fewer moving parts.
Over the last 8 years, I’ve worked across AI, backend, and cloud, building systems used by real
businesses:
→ AI recommendation engine serving 50,000+ Shopify stores with millions of daily requests
→ PCI-compliant financial platform with real-time processing and multi-tenant architecture
→ AI-powered accounting SaaS using RAG Pipeline and semantic search
→ Multi-tenant logistics and eCommerce platforms with full Workflow Automation
What I can help you with:
→ AI Automation and Workflow Automation to replace manual work
→ AI Agent Development and Multi-Agent Systems for real business use cases
→ AI integration into SaaS products, CRMs, and internal tools
→ Generative AI, AI Chatbot Development, and Conversational AI systems
→ Scalable AI App Development using Serverless Architecture (AWS Lambda, Amazon Bedrock)
→ End-to-end API Integration across third-party services and platforms
→ Production-ready systems with proper CI/CD Pipeline, performance, and cost optimization
Why clients work with me:
→ Focus on production ready AI products, not prototypes or experiments
→ Strong in AI + backend + AWS (not just one piece)
→ Experience with systems handling millions of requests and real users
→ Clean architecture, scalability, and long-term maintainability
Core stack:
AI: Generative AI, AI Agents, RAG Pipeline, LangChain, OpenAI, Claude, Amazon Bedrock
Backend: Node.js, Python, REST, GraphQL
Cloud: AWS Lambda, DynamoDB, Step Functions, EventBridge
Automation: Workflow Automation, CRM Automation, API Integration
Infra: CI/CD Pipeline, Docker, Kubernetes
If you're building an AI product on AWS and need someone to handle end-to-end product development with scalability, cost efficiency, and long-term architecture in mind, let’s talk.
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.
RAG Review & Optimization Plan
I will review your retrieval flow, data structure, model usage, and response quality, then prepare a clear optimization plan.