You will get a RAG Document Intelligence System for instant knowledge base Q&A
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Project details
Your documents hold the answers your team needs every day. The problem is finding them fast enough. This RAG Document Intelligence System gives your organization an AI assistant that reads your PDFs, policy docs, manuals, and knowledge bases, then answers any question in under 3 seconds with exact source citations.
Built with LangChain, Pinecone, GPT-4, and FastAPI, the system ingests your documents, chunks and embeds them into a vector database, and retrieves only the most relevant context before generating an answer. No hallucinations. No guesswork. Every answer is grounded in your actual documents with a source reference attached.
What you get: a production-ready RAG system with a chat interface, REST API, multi-turn conversation memory, document ingestion pipeline, and full deployment on Railway or your preferred cloud. Source code, API documentation, and a walkthrough are included in every tier.
Live demo available at rag.datawebify.com. Typical results: 98% cost reduction vs manual lookup, query resolution under 3 seconds, and 24/7 availability with zero downtime.
Built with LangChain, Pinecone, GPT-4, and FastAPI, the system ingests your documents, chunks and embeds them into a vector database, and retrieves only the most relevant context before generating an answer. No hallucinations. No guesswork. Every answer is grounded in your actual documents with a source reference attached.
What you get: a production-ready RAG system with a chat interface, REST API, multi-turn conversation memory, document ingestion pipeline, and full deployment on Railway or your preferred cloud. Source code, API documentation, and a walkthrough are included in every tier.
Live demo available at rag.datawebify.com. Typical results: 98% cost reduction vs manual lookup, query resolution under 3 seconds, and 24/7 availability with zero downtime.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$699
|
Standard
$1,400
|
Advanced
$2,800
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 18 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 Agent Developer and Architect | Multi-Agent Systems | LangGraph n8n
100%
Job Success
Sialkot, Pakistan - 8:24 pm local time
🎙️ AI Voice Agents (Vapi, ElevenLabs, Twilio)
⚙️ Workflow Automation (n8n, Make, GoHighLevel)
🧠 LLM Integration (GPT-4, Claude, Gemini, LLaMA)
🔗 API Development & System Integration
👨💻 5+ Years Building Production AI Solutions
Businesses come to me when they need autonomous AI systems that actually run in production, not demos or prototypes.
I build multi-agent pipelines, AI voice agents, and intelligent workflow automations using Python, LangGraph, CrewAI, FastAPI, and the full LLM stack (GPT-4o, Claude, Gemini). Everything I deliver is Docker-containerized, deployed on Railway or AWS, monitored, and documented.
Recent results from live production systems:
- 77% cost reduction on customer support operations ($12K to $2.8K/month)
- 95% faster invoice processing with 98%+ GPT-4o extraction accuracy
- 85% faster appointment booking with 100% after-hours coverage
- 83% time savings on bookkeeping automation ($1,200 to $130/month)
WHAT I BUILD
Multi-Agent Systems
LangGraph and CrewAI orchestration, tool-calling agents, memory management, self-evaluation loops, MCP and A2A protocol integration, Pydantic AI, AutoGen. Built for enterprise scale with full audit trails and human-in-the-loop escalation.
AI Voice Agents
Inbound and outbound AI calling via Vapi and Twilio. ElevenLabs voice synthesis. Real-time STT/TTS. Lead qualification, appointment booking, 24/7 receptionist automation. GoHighLevel and HubSpot CRM sync.
Workflow Automation with AI Decision Layers
n8n agent workflows, Make scenarios, GoHighLevel pipeline automation. The difference from standard automation: every workflow has an LLM decision layer, not just if/then logic.
RAG and Document Intelligence
Semantic chunking, hybrid search, reranking with Pinecone and pgvector. GPT-4o vision for invoice and document extraction. Three-way matching, ERP sync (QuickBooks, Xero, SAP, NetSuite). AP automation with 98%+ accuracy.
Production Infrastructure
FastAPI backends, PostgreSQL and Supabase, Redis, Docker, Railway, AWS. Every system ships with monitoring, error handling, and documentation. Not a side project setup.
USE CASES
AI receptionists and intake agents | Appointment booking and rescheduling | Lead qualification and CRM automation | Customer support automation | Accounts payable and invoice processing | Facility management ops | Recruitment screening | Sales pipeline automation | B2B lead generation | WhatsApp and SMS automation
TECH STACK
Agents: LangGraph, CrewAI, AutoGen, Pydantic AI, LangChain
LLMs: GPT-4o, Claude 3.5/3.7, Gemini 2.5, Llama 3, Mistral
Voice: Vapi, ElevenLabs, Twilio, Bland.ai
Automation: n8n, Make, GoHighLevel
Backend: Python, FastAPI, REST APIs
Data: PostgreSQL, Supabase, MongoDB, Redis, Pinecone, Chroma
Infra: Docker, Railway, AWS, GitHub CI/CD
Integrations: QuickBooks, Xero, SAP, Stripe, PandaDoc, Google Calendar, HubSpot
If you are building an AI system that needs to run reliably in production, 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.
Document Ingestion and Setup
Collect your documents, set up the vector database, chunk and embed all content into Pinecone. Configure the retrieval pipeline and test coverage.
RAG Pipeline and Chat Interface
Build the LangChain retrieval chain, integrate GPT-4, add multi-turn memory, and connect the chat UI or API endpoint. Test accuracy across document types.