You will get an AI Knowledge Base With RAG Document Q&A
Project details
Turn your company documents into an AI-powered Q&A assistant that gives instant, accurate answers with source citations.
No more searching through hundreds of pages. Your team or customers upload documents and ask questions in natural language — the AI reads, understands, and answers from your actual data.
How it works:
Documents are parsed and split into searchable chunks. When a question comes in, the system finds the most relevant sections and sends them to the AI along with the question. The AI generates a precise answer and shows exactly which parts of the document it used.
Use cases:
• Employee onboarding knowledge base
• Customer self-service from product documentation
• Legal document analysis and clause extraction
• Research paper summarization and Q&A
• Internal policy and compliance assistant
Tech stack: Next.js 15, TypeScript, Python, LLM APIs (OpenAI/Claude/DeepSeek), vector search, PostgreSQL, Tailwind CSS.
I've built and deployed a working RAG system — see my portfolio for a live demo. Every delivery includes full source code, documentation, and deployment to your domain.
No more searching through hundreds of pages. Your team or customers upload documents and ask questions in natural language — the AI reads, understands, and answers from your actual data.
How it works:
Documents are parsed and split into searchable chunks. When a question comes in, the system finds the most relevant sections and sends them to the AI along with the question. The AI generates a precise answer and shows exactly which parts of the document it used.
Use cases:
• Employee onboarding knowledge base
• Customer self-service from product documentation
• Legal document analysis and clause extraction
• Research paper summarization and Q&A
• Internal policy and compliance assistant
Tech stack: Next.js 15, TypeScript, Python, LLM APIs (OpenAI/Claude/DeepSeek), vector search, PostgreSQL, Tailwind CSS.
I've built and deployed a working RAG system — see my portfolio for a live demo. Every delivery includes full source code, documentation, and deployment to your domain.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging FaceAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$600
|
Standard
$2,000
|
Advanced
$4,500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 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 |
Frequently asked questions
About Kelvin
AI Integration & Full-Stack Developer | Chatbots, RAG Systems, API
Manchester, United Kingdom - 4:09 am local time
What I deliver:
→ AI chatbots & virtual assistants (Next.js + OpenAI/Claude/DeepSeek API)
→ RAG document Q&A systems with vector search & source citations
→ AI agent development & multi-step workflow automation
→ AI integration into existing SaaS platforms & legacy systems
→ Automated data pipelines & real-time processing systems
→ API integrations connecting multiple services (CRM, Slack, email, databases)
→ Full-stack web applications with AI features built in
→ n8n / Zapier / Make automation workflows with AI
My background is in building real-time systems that handle live data. I've independently designed and deployed automated bots that process market data from multiple API sources (REST + WebSocket), execute decisions based on custom logic, and run 24/7 on VPS infrastructure. This means I understand production reliability — not just getting code to work, but keeping it running.
Tech stack: Next.js, React, TypeScript, Node.js, Python, FastAPI, LangChain, PostgreSQL, Supabase, Pinecone, OpenAI API, Claude API, DeepSeek API, Vercel AI SDK, WebSocket, Docker, Linux/VPS deployment.
I respond fast, deliver faster, and write clean code with documentation.
Steps for completing your project
After purchasing the project, send requirements so Kelvin can start the project.
Delivery time starts when Kelvin receives requirements from you.
Kelvin works on your project following the steps below.
Revisions may occur after the delivery date.
Document Review & Architecture
I review your documents, plan the chunking strategy and retrieval approach, and confirm the tech stack with you.
Build RAG System
Build the document parser, retrieval engine, AI Q&A interface, and source citation system. You'll see a working prototype.