You will get Add a RAG-Powered AI Chatbot to Your Website or App
Project details
Add a RAG-Powered AI Chatbot to Your Website or App
I'll integrate a fully functional AI chatbot into your existing product, trained on your own data using Retrieval-Augmented Generation (RAG) and LangChain.
What you get:
• AI that answers from YOUR data — documents, FAQs, or database
• Built with LangChain + vector store (Pinecone, ChromaDB, or Weaviate)
• Choice of model: GPT-4, Claude, or Llama 3 (open-source, lower cost)
• Chat UI component (React/Next.js) — clean, responsive, brandable
• Deployed on your existing infrastructure (GCP, Vercel, Railway)
Packages:
Starter · $400 — 1 data source, basic chat UI, deployed (3 days)
Standard · $800 — up to 10 sources, custom branding, chat history (5 days)
Premium · $1,200 — unlimited sources, multilingual, admin panel, 30-day support (7 days)
Ideal for: SaaS apps, customer support bots, internal knowledge bases, booking platforms.
I'll integrate a fully functional AI chatbot into your existing product, trained on your own data using Retrieval-Augmented Generation (RAG) and LangChain.
What you get:
• AI that answers from YOUR data — documents, FAQs, or database
• Built with LangChain + vector store (Pinecone, ChromaDB, or Weaviate)
• Choice of model: GPT-4, Claude, or Llama 3 (open-source, lower cost)
• Chat UI component (React/Next.js) — clean, responsive, brandable
• Deployed on your existing infrastructure (GCP, Vercel, Railway)
Packages:
Starter · $400 — 1 data source, basic chat UI, deployed (3 days)
Standard · $800 — up to 10 sources, custom branding, chat history (5 days)
Premium · $1,200 — unlimited sources, multilingual, admin panel, 30-day support (7 days)
Ideal for: SaaS apps, customer support bots, internal knowledge bases, booking platforms.
AI Algorithms
Large Language ModelAI Applications
AI ChatbotAI Development Language
PythonAI Models
GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$400
|
Standard
$800
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$75
Additional Revision
+$40Frequently asked questions
About Carlos
Full-Stack AI Engineer | LangChain RAG Pipelines Next.js N8N Autom
Barquisimeto, Venezuela - 11:41 pm local time
My most recent project, Mequedo, is a live booking platform where an AI concierge recommends accommodations based on natural language input. Built end-to-end: Next.js + TypeScript, Django/DRF, LangChain RAG pipeline powered by Llama 3 via NVIDIA API.
I also served as Lead Software Engineer at One Community Global (US nonprofit), contributing 63 commits to a production MERN dashboard used across the organization.
What I build:
• Conversational AI agents with custom knowledge bases (LangChain, RAG, vector DBs)
• AI-powered web apps (Next.js, TypeScript, React, Python/Django)
• Business automation workflows (N8N, Make)
• Real-time features (Socket) and payment integrations (PayPal)
• REST APIs with Express.js and Django REST Framework
Stack: TypeScript · Node.js · Python · Next.js · React · Django · LangChain · RAG · Llama 3 · NVIDIA API · N8N · Make · Socket · MongoDB · GCP
English C1 — clear async communication, no repeated back-and-forth.
If you need an AI feature shipped to production, a RAG chatbot trained on your own data, or automation that saves your team hours every week — I deliver on time.
Steps for completing your project
After purchasing the project, send requirements so Carlos can start the project.
Delivery time starts when Carlos receives requirements from you.
Carlos works on your project following the steps below.
Revisions may occur after the delivery date.
Document review & kickoff
Day 1 I review your uploaded files and intake answers, flag any data gaps or formatting issues, and confirm the chatbot scope and timeline with you before writing a single line of code. You will know exactly what gets built and when.
Data processing & embedding
Day 1–2 Your documents are cleaned, split into optimized chunks, and indexed into the vector store (Pinecone or ChromaDB). This is the foundation of the whole system — well-indexed data means accurate, fast answers and fewer hallucinations.