You will get a custom RAG AI Chatbot from your own data
Project details
Turn your unstructured company data into an instant-answer machine. If you have scattered customer feedback, dense PDF manuals, product documentation, or raw customer complaint logs, I will build a secure, intelligent Retrieval-Augmented Generation (RAG) chatbot tailored entirely to your business.
What sets this project apart is precision. Many generic chatbots hallucinate or leak data. My approach builds a deterministic pipeline that anchors the LLM (OpenAI, Claude, or Llama) strictly to your source text. This guarantees factual, context-aware answers with zero guesswork.
What you get with this service:
• Custom Data Ingestion: Advanced parsing and chunking strategies optimized for complex files and layouts.
• Reliable Context Retrieval: Semantic search configuration via vector databases to serve accurate matching data to the LLM instantly.
• Seamless Deployment: An intuitive web interface, or direct connection into your internal communication tools like Slack or Discord.
Stop wasting hours digging through endless files. Let's convert your static documents into an automated utility that slashes support times and unlocks trapped internal knowledge instantly.
What sets this project apart is precision. Many generic chatbots hallucinate or leak data. My approach builds a deterministic pipeline that anchors the LLM (OpenAI, Claude, or Llama) strictly to your source text. This guarantees factual, context-aware answers with zero guesswork.
What you get with this service:
• Custom Data Ingestion: Advanced parsing and chunking strategies optimized for complex files and layouts.
• Reliable Context Retrieval: Semantic search configuration via vector databases to serve accurate matching data to the LLM instantly.
• Seamless Deployment: An intuitive web interface, or direct connection into your internal communication tools like Slack or Discord.
Stop wasting hours digging through endless files. Let's convert your static documents into an automated utility that slashes support times and unlocks trapped internal knowledge instantly.
Programming Languages
JavaScript, Python, TypeScriptCoding Expertise
Cross Browser & Device Compatibility, Performance Optimization, SecurityWhat's included $600
These options are included with the project scope.
$600
- Delivery Time 5 days
- Number of Revisions 2
- Number of Pages 5
- Content Upload
- Responsive Design
Optional add-ons
You can add these on the next page.
Additional Page
(+ 1 Day)
+$100Frequently asked questions
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SD
Shin D.
Jun 30, 2026
Mongolian Translator Needed for AI Training
About Munkh
Full-Stack Developer | React & Next.js | SaaS & Dashboards
Ulan Bator, Mongolia - 3:15 am local time
What sets me apart is the combination of strong frontend craft and solid backend architecture. I've built everything from bilingual e-learning platforms and pediatric clinic management systems to fintech dashboards, police department portals, and social networking apps — across industries and complexity levels. I also bring GIS and map-based application experience using Leaflet, WebGL, and PostGIS, which is rare in the full-stack space.
My core stack: React, Next.js, Vue, TypeScript, Node.js, NestJS, PostgreSQL, Supabase, Firebase, Tailwind CSS.
I hold HackerRank certifications in Frontend Development (React), CSS, SQL, and Problem Solving. My portfolio spans 9+ shipped projects across fintech, healthcare, education, logistics, and public sector clients — including a metropolitan police department platform, a university management system, and a financial card administration tool. I write clean, maintainable code and communicate proactively throughout every project.
If you have a project in mind — whether it's a new build or an existing codebase that needs improvement — send me a message and I'll get back to you within a few hours to discuss the details.
Steps for completing your project
After purchasing the project, send requirements so Munkh can start the project.
Delivery time starts when Munkh receives requirements from you.
Munkh works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit & Architecture Planning
I review your unstructured data, documents, and PDFs. I map out the data pipeline, select the optimal chunking strategy, and choose the right vector database setup tailored to your specific file types.
RAG Pipeline & Embeddings Development
I write the Python backend scripts to clean, parse, and embed your data into the vector store. I implement the core retrieval mechanisms to ensure the AI fetches accurate context without hallucinating.