You will get a RAG AI Assistant Using Your Documents & Knowledge Base
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Project details
Turn your documents and internal knowledge into an AI Assistant that can actually find and use the right information.
I’ll build a RAG (Retrieval-Augmented Generation) system that connects your documents and knowledge base to an LLM, using vector databases and knowledge retrieval to deliver relevant, context-aware answers.
Whether you need an internal knowledge assistant, customer support AI, document Q&A, or a searchable company knowledge base, I can build the RAG pipeline around your data and use case.
What I can build:
• RAG AI Assistant
• Document Q&A & knowledge retrieval
• LLM integration
• Vector database & embeddings
• Semantic search
• Knowledge base integration
• AI-powered search
• Custom retrieval workflows
The goal is simple: make your information searchable and useful through AI—not just upload documents into a chatbot.
Have a knowledge base you want to turn into an AI Assistant? Send me your documents and requirements, and let's discuss it.
I’ll build a RAG (Retrieval-Augmented Generation) system that connects your documents and knowledge base to an LLM, using vector databases and knowledge retrieval to deliver relevant, context-aware answers.
Whether you need an internal knowledge assistant, customer support AI, document Q&A, or a searchable company knowledge base, I can build the RAG pipeline around your data and use case.
What I can build:
• RAG AI Assistant
• Document Q&A & knowledge retrieval
• LLM integration
• Vector database & embeddings
• Semantic search
• Knowledge base integration
• AI-powered search
• Custom retrieval workflows
The goal is simple: make your information searchable and useful through AI—not just upload documents into a chatbot.
Have a knowledge base you want to turn into an AI Assistant? Send me your documents and requirements, and let's discuss it.
AI Development Type
Deep Learning, Knowledge RepresentationAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$199
|
Standard
$499
|
Advanced
$999
|
|---|---|---|---|
| Delivery Time | 3 days | 4 days | 7 days |
Number of Revisions | 2 | 3 | 4 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Frequently asked questions
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WL
Wiley L.
Jun 12, 2026
Python Developer needed to build an AI Book Update Agent (LangChain/LangGraph)
Zakria is truly talented and went to extreme efforts to perfect the agent. I hope to work with him again.
BH
Bill H.
Jun 10, 2026
Software Developer – Reporting, Integrations & AI
Zakria is reliable and talented. I will be using him again in the near future.
About Zakria
AI/ML Engineer | LLMs, Generative AI, RAG, FastAPI & Python Expert
100%
Job Success
Worcester, United States - 12:18 am local time
Over the past 8+ years, I have developed AI products, healthcare platforms, CRM systems, workflow automation tools, and enterprise applications with a strong focus on Python-based backend development and machine learning solutions.
My expertise includes building AI agents, Retrieval-Augmented Generation (RAG) systems, LLM-powered applications, document intelligence platforms, and production-ready AI infrastructure that scales.
### Core Expertise
✅ Generative AI & LLM Applications
• OpenAI, Claude, Gemini Integration
• LangChain & LangGraph Development
• AI Agents & Multi-Agent Systems
• Retrieval-Augmented Generation (RAG)
• Conversational AI & Chatbots
• Knowledge Bases & Semantic Search
• Prompt Engineering & Evaluation
• AI Workflow Automation
✅ Machine Learning & NLP
• Predictive Analytics
• Recommendation Systems
• Natural Language Processing (NLP)
• Classification & Regression Models
• Data Processing & Feature Engineering
• Model Training & Deployment
• MLOps & Monitoring
• Custom ML Pipelines
✅ Python Backend Development
• FastAPI
• Django
• REST APIs
• Microservices Architecture
• Async Processing
• Background Jobs & Task Queues
• Third-Party API Integrations
• Scalable Backend Systems
### Selected Project Experience
🚀 AI-Powered Healthcare Platform
Developed HIPAA-compliant healthcare solutions with AI-assisted workflows, document processing, reporting automation, and patient data management systems.
🚀 AI Voice Agent Platform
Built a multi-tenant AI voice platform that automates customer support, appointment booking, lead qualification, and outbound calling workflows using LLMs and conversational AI.
🚀 Enterprise RAG & Knowledge Management System
Designed and implemented document intelligence systems capable of processing large document collections, enabling semantic search, enterprise knowledge retrieval, and AI-powered question answering.
🚀 AI CRM & Workflow Automation Platform
Built an AI-powered CRM with intelligent lead management, automated reporting, workflow orchestration, and business process automation.
🚀 Document Processing & AI Automation
Developed AI-powered systems for document extraction, classification, validation, summarization, and structured data generation.
### Technical Stack
Python • FastAPI • Django • LangChain • LangGraph • OpenAI • Claude • Gemini • RAG • AI Agents • NLP • Machine Learning • PostgreSQL • MongoDB • Redis • Pinecone • Weaviate • FAISS • Celery • Docker • AWS • Linux • Git • CI/CD
### Why Clients Hire Me
• Strong Python & AI Engineering Background
• Production-Ready AI Systems, Not Just Prototypes
• Experience Building AI SaaS Products End-to-End
• Scalable Backend Architecture Expertise
• Clear Communication & Reliable Delivery
• Focus on Business Outcomes and ROI
Whether you're building an AI startup, implementing LLM-powered features, creating AI agents, developing machine learning models, or integrating Generative AI into an existing platform, I can help design, build, and deploy scalable solutions that deliver measurable business value.
Steps for completing your project
After purchasing the project, send requirements so Zakria can start the project.
Delivery time starts when Zakria receives requirements from you.
Zakria works on your project following the steps below.
Revisions may occur after the delivery date.
Knowledge Base & Requirements
Review your documents, knowledge sources, AI assistant requirements, and desired user experience to define the RAG approach.
Document Processing & Vector Database
Process your documents, create embeddings, and set up the vector database for efficient knowledge retrieval.