Custom AI Chat Platform Development

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Your Private AI Chat Platform with Custom Documents & Videos ❇️ Hi My Friend, hope you are doing well. I reviewed your project requirements and see you are looking for an experienced AI/LLM/RAG developer. Look no further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for AI chat platforms. I will create a private ChatGPT-like system that answers questions using only your uploaded content, ensuring no internet browsing. ➡️ Why Me? I can easily build your private AI chat platform as I have 5 years of experience in AI development, specializing in LLMs and RAG systems. My expertise includes Python programming, database management, and user authentication. I also have a strong grip on technologies like LangChain and FastAPI, ensuring a robust solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail. I can show you samples of my previous work and how I can efficiently meet your requirements. Looking forward to our chat! ➡️ Skills & Experience: ✅ Python Development ✅ LLM Integration ✅ RAG Systems ✅ User Authentication ✅ AWS Deployment ✅ FastAPI Framework ✅ Database Management ✅ Content Indexing ✅ Docker Deployment ✅ Frontend Development ✅ User Management ✅ Open Source Models Waiting for your response! Best Regards, Zohaib

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@Herculesz

Hi, this project involves building a custom AI chat platform leveraging LLMs and vector databases, which aligns well with my experience in AI orchestration and backend development. The main engineering risk lies in orchestrating reliable prompt handling and efficient retrieval to maintain low latency and high relevance in chat responses. I usually structure systems by separating ingestion, retrieval, and response layers to ensure modularity and scalability. I've built several production systems with FastAPI backends and vector search that handle complex AI workflows. My work on the TikTok AI Livestream Setup involved real-time AI-driven interactions and orchestration, while Custom Feature Development & Integration demonstrates my ability to enhance backend and database layers for tailored functionality. I approach LLM reliability with grounding checks and fallback routing to maintain response quality under varying input conditions. These systems are designed for long-term production use with maintainability and scalability in mind. I can start by outlining the retrieval pipeline and mapping the agent flow to ensure robust prompt engineering and vector search integration. Thanks, Hercules

@corpmember29

Hello, I WILL build a private AI chat platform that answers exclusively from your uploaded content (PDFs, DOCX, TXT, EPUB, videos). The system will be fully open-source, Dockerized, and deployable on AWS with no paid APIs required. Key Features: ChatGPT-style interface with optional history User authentication and management Admin dashboard to upload and index documents/videos RAG backend using LangChain/LlamaIndex + ChromaDB/Qdrant/FAISS AI responses strictly from uploaded content; says “not found” when info is missing Simple React/Vue frontend + FastAPI backend Tech Stack: Python, LangChain/LlamaIndex, ChromaDB or Qdrant, FastAPI, React/Vue, Docker, open-source LLMs (e.g., MPT, Falcon, LLaMA 2) Deliverables: Full source code Dockerized deployment on your AWS server Complete installation guide and low-maintenance setup I have prior experience in RAG/LLM projects and can ensure a scalable, functional, low-cost solution that works entirely with your private documents and videos. THaks

@OlektraGroup

Hi. You need a private, air-gapped RAG system that strictly restricts LLM responses to your proprietary documents and videos without external web access. I recently built a similar offline document-query engine for a research client using LlamaIndex and ChromaDB, ensuring zero data leakage by stripping all internet-facing API calls. To achieve your "answer only from source" requirement, I will implement a custom prompt-engineering layer with a high-temperature penalty for hallucinations, forcing the model to return "Information not found" if the context vector similarity falls below a strict threshold. My background in deploying CNNs and ONNX models on AWS ensures I can containerize this pipeline for your server efficiently. What is the approximate total volume and file format breakdown of your knowledge base?

@ShaikhAneesa

Hi there, I understand you need a private, self-hosted RAG-based AI chat platform that works like ChatGPT but only answers using your uploaded PDFs, DOCX, EPUBs, videos, and documents, with zero internet browsing. I am confident I can build a clean, low-cost, fully open-source system deployable on your AWS server. My approach will be a Python-based FastAPI backend with a RAG pipeline using LangChain or LlamaIndex. All uploaded content will be processed, chunked, and embedded into a vector database such as Qdrant or FAISS. A local LLM (Llama 3 or Mistral via Ollama/vLLM) will generate responses strictly grounded in retrieved context, and will explicitly state when no relevant information exists. The frontend will be a simple ChatGPT-style React UI with login, optional chat history, and an admin dashboard for uploading files, re-indexing content, and managing users. Video/YouTube links can be transcribed and indexed. The system will be fully Dockerized for easy AWS deployment and long-term maintainability. Deliverables include full source code, Docker setup, RAG pipeline, admin panel, frontend UI, and installation documentation. No paid APIs will be required, and the architecture will be optimized for low-cost CPU or optional GPU scaling depending on usage. Before I proceed, do you want prioritization on lowest AWS cost or faster response time at scale? I’m ready to start immediately. Warm Regards, Aneesa.

@DeveloperStation

Hi there, We’ve developed a similar product called Descripio, where we built a Chrome extension that extracts video transcripts from YouTube and uses them to answer questions. We also created a custom LLM model that can be fine-tuned with specific data, allowing it to answer questions based on uploaded documents. For your project, we can use open-source LLMs like Llama 2 or Falcon, which can be fine-tuned with your data to provide accurate answers. We can also implement a feature that allows the model to answer questions based on both uploaded documents and web searches, giving you the best of both worlds. We can schedule a 10-minute introductory call to discuss your project in more detail and see if I’m the right fit for your needs. I’m looking forward to hearing more about your exciting project. Best, Adil

@topcreator100

Hi there, I'm excited to build your private, non-internet AI chat platform that answers only from your uploaded content. I'll implement a local, open-source RAG stack using LangChain, ChromaDB, and an LLM deployed via Ollama/vLLM on AWS, with strict content sourcing from PDFs, videos, and docs. I'll create a simple React frontend with authentication and optional chat history, plus a FastAPI backend that handles indexing and re-indexing of uploaded content. I have several experience with similar projects and will follow your requirements to ensure no web searches and explicit 'not found' responses when data isn't present. Next steps: I can start with a minimal viable setup within two weeks and deliver fully dockerized code and installation docs.

@dfordesigners

Hi there, I specialize in AI development and have extensive experience with RAG and LLM projects. I propose using Python, LangChain, Ollama, ChromaDB, and FastAPI for your custom AI chat platform. I can ensure the AI only generates responses from uploaded content, meeting your requirements. With a focus on open-source models and minimal recurring costs, I guarantee a functional and scalable solution. Let's discuss your project further to align on technology and approach. Looking forward to the opportunity to work on this exciting project with you. Best regards, Kausar | AI Developer

@yuliussmayoru

Hello! I’ve found the best approach to build your private AI chat platform that only answers using your uploaded documents and videos, ensuring it’s fully self-contained, scalable, and low-maintenance. I’ll start by designing a backend with Python using FastAPI, integrating LangChain or LlamaIndex for building a Retrieval-Augmented Generation pipeline, and connecting it to a vector database like ChromaDB, Qdrant, or FAISS for semantic search across uploaded content. Users will interact via a clean React-based ChatGPT-style frontend with optional login and chat history, while an admin dashboard allows uploading PDFs, DOCX/TXT, EPUBs, and YouTube/video links, with automated indexing and re-indexing. The AI will answer strictly from uploaded sources; if no relevant information exists, it will respond explicitly that it cannot find an answer. Open-source models like vLLM or Ollama will handle generation, avoiding any paid APIs. The system will be fully Dockerized for deployment on your AWS instance, with installation documentation, SSH-ready setup, and guidance for minimal recurring maintenance. Efficient resource use will be ensured, with vector indexes optimized for search speed while keeping costs low. Security, user management, and a straightforward admin workflow will be included, allowing you to manage content and users easily. Warm regards, Yulius Mayoru

@sabaehsan

Greetings, It looks like you're aiming to create a private AI chat platform that solely relies on your specific documents and videos, without any external internet access. This is a great way to ensure your AI assistant is tailored to your proprietary content. I can help you build a user-friendly chat interface and a robust backend to manage all the uploads and indexing of your materials, ensuring the AI only pulls from the content you provide. With experience in developing AI solutions and using technologies like Python, LangChain, and FastAPI, I can deliver a scalable and low-maintenance system that meets your needs. I’ll ensure that the architecture is straightforward, with clear installation documentation. I’m excited about the opportunity to work on this project with you. Best regards, Saba Ehsan

@ykarora26

With experience in AI development and LLM, I understand your need for a private AI chat platform based solely on custom content. My past projects include building similar AI knowledge systems. How do you envision user access control within the platform to ensure content security and privacy? Regards, Yogesh Kumar

@SmartCodeDev

Hello, I can build your private RAG based AI platform fully on your AWS server using open source models and without relying on internet search or mandatory paid APIs. The system will include a ChatGPT style interface, secure login, admin dashboard, document and video ingestion, automatic indexing, and retrieval based responses strictly limited to uploaded content only. I would recommend a stack using Python, FastAPI, LangChain or LlamaIndex, Qdrant or ChromaDB, and Ollama or vLLM with models like Llama 3 or Mistral depending on your server capacity and response requirements. The platform will be fully Dockerized, scalable, low maintenance, and designed so the AI explicitly states when information is not available in the uploaded sources. I have experience building private RAG systems, document intelligence workflows, vector search pipelines, and self hosted LLM environments, and I can also guide you on AWS hardware sizing for optimal performance within budget.

@mayankinnovative

Hi, As per my understanding: You need a private, fully self-hosted RAG-based AI chat platform that behaves like ChatGPT but answers strictly from your uploaded documents and videos only. The system must avoid all internet access/search behavior, run entirely on your AWS infrastructure, support open-source models, remain low-maintenance, and provide scalable document ingestion plus conversational querying over proprietary knowledge sources. Implementation approach: I would recommend a Python-based architecture using FastAPI for backend APIs, React for the frontend chat interface, LangChain/LlamaIndex for orchestration, and Qdrant or ChromaDB for vector storage. For local inference, Ollama or vLLM with models like Llama 3, Mistral, or Qwen would provide strong performance while avoiding paid APIs. The ingestion pipeline will support PDFs, DOCX, EPUB, TXT, and video transcript extraction from YouTube/local uploads with automatic chunking, embedding, indexing, and re-indexing workflows. Strict retrieval grounding will be enforced so the assistant only answers from indexed sources and explicitly responds when information is unavailable. The platform will include authentication, optional chat history, admin dashboard, Dockerized deployment, and complete installation documentation optimized for AWS deployment with low recurring costs and maintainable infrastructure. A few quick questions: 1. Approximately how many documents/videos will be indexed initially?

@teodorgeorge

Hello, I can build your private AI chat platform so it answers only from your uploaded PDFs, books, DOCX/TXT files, and video transcripts, with no web browsing or paid API dependency. I have experience with RAG systems using Python, FastAPI, LangChain/LlamaIndex, vector databases like ChromaDB/Qdrant/FAISS, and local open-source models through Ollama or vLLM. For your AWS setup, I would suggest a Dockerized FastAPI backend, React chat UI, admin upload/indexing dashboard, Qdrant or ChromaDB for retrieval, and a model such as Llama 3.1 8B or Mistral depending on server resources. I will also include source code, installation documentation, re-indexing, user login, and fallback behavior that clearly says when the answer is not found in uploaded content. I am ready to begin immediately and would be happy to discuss the project in further detail. Thanks, Teo

@alizain721

I see you need a closed knowledge assistant that answers only from your uploads and never queries the web. The core challenge is strict source grounding plus a lightweight, maintainable stack you can run on one AWS instance. Most failures come from loose retrieval pipelines or models allowed to hallucinate beyond the index. I build systems that enforce source only answers and an explicit fallback when nothing is found. Built CrowdAxis, a FastAPI ML platform that ingests multiple sources, normalizes data, and serves model endpoints for real time queries. My plan for an MVP ✓ LangChain for orchestration with Qdrant for vectors ✓ vLLM or Ollama hosted Llama 2 / Mistral quantized model ✓ FastAPI backend, React simple chat UI, Dockerized deployment on your AWS instance Previous RAG experience: CrowdAxis. Recommended model: Mistral 7B quantized or Llama 2 13B depending on GPU. Timeline: MVP 2 weeks, docs and tweaks 1 week. Budget: 140 USD. AWS estimate: one GPU server with 24GB VRAM, 4 CPU cores, 32 64 GB RAM and 200 GB SSD for storage and indexes. Can you share sample files size and grant SSH access so I can finalize sizing and start indexing?

@mkh559a5a1fd66e9

Being a seasoned Python developer with extensive experience in LLM Fine Tuning, Machine Learning, and Automation, I believe I am the right candidate to undertake the project of developing a custom AI Chat Platform for you. Not only do I have strong foundational skills in core Python, but I'm also well-versed in data manipulation - an important skill for this project given the content-heavy nature of the AI's responses in your request. My previous experiences in S100D Document Processing for Defense and Aerospace showcase my expertise in handling large volumes of file formats and content. Drawing from these experiences, I can confidently assure you that the AI I'll develop will be fully reliant on YOUR content - no internet browsing involved, just pure personalized outputs based on precisely what you feed it. To ensure both efficiency and cost-effectiveness, I propose we utilize open-source models like LangChain or LlamaIndex which eliminate any recurring monthly costs. In terms of technology stack, FastAPI coupled with React/Vue is my recommendation; they're reliable and scalable tools that will guarantee user-friendly interface both for you as an admin and potential users. Let's make this happen within your budget and timeline!

@sufyanasmat38

I have experience building private RAG based AI systems using Python, LangChain, vector databases, and open source LLMs for document driven question answering platforms where all responses must remain restricted to uploaded knowledge sources only. For your platform, I can build a secure ChatGPT style interface with authentication, document and video ingestion, automated indexing, re indexing support, and a private admin dashboard running fully on your AWS infrastructure. I am comfortable working with technologies such as FastAPI, LangChain or LlamaIndex, Qdrant or ChromaDB, and Ollama or vLLM based open source models to create a scalable low maintenance architecture without dependency on paid APIs or internet browsing. The system can be fully Dockerized and designed so the AI strictly answers from uploaded PDFs, books, DOCX files, and processed video transcripts, while clearly responding when information is not available in the knowledge base. I can also provide installation documentation, AWS hardware recommendations, and a clean deployment workflow focused on long term maintainability and low operational cost.

@riogushiken1114

Hi there, I reviewed your project carefully and can help you build a private ChatGPT-style RAG platform that answers only from your uploaded PDFs, books, docs, and video content. Why I’m a good fit: • Strong experience with Python, FastAPI, LangChain/LlamaIndex, vector databases, and open-source LLM deployment • Built RAG systems with source-restricted answers, re-indexing, admin upload flows, and “not found in sources” guardrails • Focus on low-maintenance, Dockerized AWS deployment with no mandatory paid APIs I’d propose FastAPI + React, LangChain or LlamaIndex, Qdrant/ChromaDB, Docker, and Ollama with Llama 3.1 8B or Mistral 7B depending on your AWS instance. For video/YouTube content, I can extract transcripts and index them cleanly. My approach: • Clean, maintainable, scalable code • Fast and clear communication • Reliable delivery with installation documentation I can start immediately and would be happy to discuss the project in more detail. Best regards,