You will get A Production Multi-Agent AI Platform with RAG and LLM
Project details
I build production-grade AI Agent Orchestration platforms not demos, not notebooks, real systems that work at scale.
AgentForge is my flagship project: a complete multi-agent AI platform built on LangGraph. It lets businesses upload documents, images, and audio, then ask questions using specialized AI agents that collaborate, validate answers, and cite sources.
What makes it different:
✦ Multi-agent pipeline — DecisionAgent routes tasks, ResearchAgent retrieves from your knowledge base, CriticAgent validates every answer
✦ Hybrid RAG — semantic + BM25 keyword search combined for superior retrieval accuracy
✦ Multimodal — handles text documents, images via Vision Agent, and audio files with Whisper transcription
✦ Web search — 3 modes: private documents only, internet only, or both with clear source labeling
✦ Voice input — speak your question instead of typing
✦ Full observability — every LLM call traced with LangSmith, token usage and cost tracked per run
Stack: LangGraph · LangChain · Groq · ChromaDB · FastAPI · React · Docker · AWS
I own the full lifecycle, architecture through deployment. No handoffs needed.
AgentForge is my flagship project: a complete multi-agent AI platform built on LangGraph. It lets businesses upload documents, images, and audio, then ask questions using specialized AI agents that collaborate, validate answers, and cite sources.
What makes it different:
✦ Multi-agent pipeline — DecisionAgent routes tasks, ResearchAgent retrieves from your knowledge base, CriticAgent validates every answer
✦ Hybrid RAG — semantic + BM25 keyword search combined for superior retrieval accuracy
✦ Multimodal — handles text documents, images via Vision Agent, and audio files with Whisper transcription
✦ Web search — 3 modes: private documents only, internet only, or both with clear source labeling
✦ Voice input — speak your question instead of typing
✦ Full observability — every LLM call traced with LangSmith, token usage and cost tracked per run
Stack: LangGraph · LangChain · Groq · ChromaDB · FastAPI · React · Docker · AWS
I own the full lifecycle, architecture through deployment. No handoffs needed.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Automatic Speech Recognition, Conversational AI, Image Analysis, Image Processing, Image Recognition, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
GitHub Copilot, Gradio, Hugging Face, StreamlitAI Models
GPT-4, LLaMA, WhisperWhat's included
| Service Tiers |
Starter
$200
|
Standard
$450
|
Advanced
$700
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 5 |
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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$70 - $150
Additional Revision
+$30
Pinecone Vector DB Integration
(+ 2 Days)
+$75
Custom Fine-Tuning Pipeline
(+ 5 Days)
+$180
Multi-Language Support
(+ 2 Days)
+$100Frequently asked questions
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
MS
Muhammad S.
Jul 8, 2026
AI Voice/Image Tool for Documentation
Excellent AI developer! built exactly what we needed, delivered on time, clean code. Will hire him again.
About Muhammad
AI Engineer | LLMs | RAG | Multi-Agent Systems | Generative AI
Islamabad, Pakistan - 1:19 am local time
With 3+ years of experience specializing in AI/ML, I design and deploy scalable AI solutions powered by LLMs, RAG architectures, multi-agent systems, NLP, and intelligent automation.
Core Expertise
✔ Generative AI & LLM Applications
✔ AI Agents & Multi-Agent Systems
✔ Retrieval-Augmented Generation (RAG)
✔ Prompt Engineering & Fine-Tuning (LoRA, QLoRA)
✔ Intelligent Document Processing (OCR + LLMs)
✔ AI-Powered Workflow Automation
✔ Conversational AI & Chatbots
✔ Semantic Search & Vector Databases
✔ Multimodal AI (Vision + Voice + Text)
✔ LLM Evaluation & Observability
✔ Machine Learning & NLP Solutions
✔ End-to-End AI Product Development
AI Technology Stack
LLMs: OpenAI GPT-4, Anthropic Claude, Groq, Gemini, Llama, Mistral
AI Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Claude Code
Vector Databases: Pinecone, Deep Lake, FAISS, Weaviate, ChromaDB, pgvector
Embeddings & Search: OpenAI Embeddings, Semantic Search, Hybrid Search, BM25, Reranking
Voice & Vision: OpenAI Whisper, GPT-4o Vision, Multimodal Processing
Observability: LangSmith, RAGAS, Model Evaluation, Prompt Versioning
Backend: Python, FastAPI, Node.js, REST APIs, PostgreSQL, Redis
Automation: n8n, Makecom, Zapier, Google Sheets API, Webhook Workflows
Cloud & DevOps: AWS (EC2, S3, RDS, Lambda, ECS), Docker, Vercel, Railway, GitHub Actions CI/CD
Projects I've Built
● AI-Powered CRM SaaS with lead pipeline and AI email generation
● Multi-Agent Real Estate Lead Engagement System
● Production Multi-Agent Orchestration Platform (AgentForge)
● AI Voice & Image Product Documentation Tool
● AI-Powered Generative Storytelling Platform
● Crypto Price Prediction with Sentiment Analysis
● Scalable Data Pipelines for Real-Time Business Intelligence
● Mystery Shopping Validation Platform (ShopMetrics + Claude)
Why Clients Hire Me
✔ Production-grade systems not demos, not notebooks
✔ End-to-end ownership from architecture to deployment
✔ Proven results like 35% API cost reduction, 99.8% uptime, $500K saved
✔ Fast delivery, complete AI SaaS shipped in 5 days
✔ Full stack, model integration through to cloud deployment
✔ Clear communication and transparent progress throughout
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.
Discovery & Architecture
Review requirements, finalize tech stack, agent design, and RAG pipeline architecture
Backend & AI Pipeline
Build FastAPI backend, RAG pipeline, vector database, and multi-agent orchestration