Custom Health & Wellness LLM

@Fazeennazar

Hello, I can build a custom health and wellness AI assistant using your hundreds of videos and articles as the knowledge base, with a retrieval-based architecture so responses stay grounded in your approved content instead of relying on generic model knowledge. I would process and transcribe the source material, structure it into searchable chunks, generate embeddings, and store them in a vector database such as pgvector, Pinecone, or Weaviate. For hosting, I recommend a secure cloud setup on AWS using a FastAPI or Node.js backend, PostgreSQL/vector storage, object storage for source files, and an LLM API or privately hosted open-source model depending on your privacy, cost, and scaling requirements. I can also build a responsive community-facing chat interface with user authentication, conversation history, citations back to source material, and administrative controls for adding or updating content. Because this is health-related, I would also include clear scope controls, safety guardrails, and content-source attribution so the system does not present unsupported information as professional medical advice. The architecture can be designed to scale from an initial community deployment into a larger platform without rebuilding the core ingestion and retrieval pipeline. Best regards

@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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@HamzaBashirr

Hi, I’m an AI Engineer specializing in LLM applications, RAG systems, AI agents, and vector databases. For your health & wellness content, I would build a RAG-based LLM system that processes the videos/articles, indexes the knowledge, and lets community users interact with it through a ChatGPT-style interface. The system can include: • Video transcription and article processing • Embeddings + vector database • LLM-powered answers grounded in your content • ChatGPT-style community interface • Secure user access • Cloud hosting and deployment • Source/document references where needed I can host it on AWS or another suitable cloud platform depending on expected usage and budget. Timeline: 7 days Bid: $500 Best regards, Hamza Bashir

@intelliresponse

As an accomplished Full-Stack developer, my skill set lends itself perfectly to your Health & Wellness LLM project. My 13+ years of experience have spanned web automation, data mining and extraction as well as AI solutions - all of which are vital when creating a system that houses and interprets hundreds of articles and videos, like your LLM requires. My Python proficiency combined with an understanding of C-based projects will be pivotal in designing your LLM's interface. Another essential aspect here concerns hosting the LLM effectively. Having a strong background in Web3 and blockchain development, I can architect robust hosting solutions for your LLM by leveraging cloud services like AWS or Google Cloud as per the scalability requirements. Partnering with me would provide you end-to-end AI-powered health and wellness LLM that caters to users' needs efficiently. By gathering bits of news, analysis or advice from numerous sources and making them easily accessible through a well-designed interface, I can turn this LLM into a powerful tool for improving people's lives. Let’s get started today!

@maxscender

I’d build this as a retrieval-augmented LLM rather than training a model from scratch. Videos are transcribed, articles parsed, then all content is chunked and embedded into a vector database. The LLM is hosted through a managed inference API for speed, or on a private GPU server if the community’s health data must stay isolated. At query time it retrieves the most relevant passages and answers with citations, keeping responses grounded in your library and reducing hallucination. The community interface would be a secure web app with login, chat, source links, feedback, and admin tools to add or remove content. Hosting runs on cloud infrastructure behind authentication, with rate limits and moderation controls so members can interact safely. This gives you one practical system for asking health and wellness questions against your own trusted video and article collection.

@rakhshanz3

With over two decades of experience in AI and machine learning, I believe my expertise and skillset make me the ideal candidate to build your Health & Wellness LLM. Drawing on my academic background in Computer Science and my industry experience, I have developed a robust understanding of natural language processing, data analysis and predictive modelling necessary for creating an effective AI tool. As the former CTO of an AI startup, I led the end-to-end development of advanced digital products similar to what you're searching for. My successful track record includes building AI-powered agents for customer support and lead qualification, developing machine learning analytics platforms for business insights and automation, as well as creating frontend web and mobile applications with scalable architecture - all skills crucial in an endeavor like yours. Moreover, my proactive approach to problem-solving ensures that from designing the database to deploying on the cloud, every aspect of your project will be meticulously attended to. I am excited about the prospect of building a robust LLM solution for your health & wellness platform. Let's connect and discuss how my skills can facilitate an exceptional user experience in your community.

@mughiraaa

hi, we can build this as a community ai platform where your videos and articles are processed into a searchable knowledge base and the llm uses that content to answer users accurately. rather than training a model from scratch, we’d recommend a rag based architecture so your content can be updated without retraining the entire model. we can host the backend and ai services on aws or another suitable cloud, with secure api access, vector storage and scalable infrastructure. we’ll also build the user interface where community members can chat with the ai, with authentication and conversation history if needed. we’ve worked on similar ai and knowledge based systems and can handle the full setup from content processing and ai integration through hosting and frontend development. can we have a quick meeting to discuss the amount of content, expected users and your preferred hosting setup?

@adriank06

I can step in as your Technical Project Manager to coordinate your team and manage tasks seamlessly using tools like Jira, Asana, or ClickUp. With a strong background in Full-Stack Development (React, JS) and ML, I not only track deadlines but also deeply understand the technical blockers your engineers might face. Quick question: What is the primary focus of the current project, and how large is the team I will be coordinating? As a 5-star rated Technical Specialist, I have successfully managed complex software pipelines and delivered projects on time. I am based in Poland (CEST time zone) and communicate clearly and proactively in fluent English. My rate is $10/hour, or we can work within your fixed-price structure once the scope is clear. Let’s chat to see if we are a great fit.

@Aaleen110

You need an LLM trained on hundreds of videos and articles with an interface for community interaction. I would start by setting up the data pipeline. I'll ingest your videos and articles, transcribing audio from videos using Whisper and processing text from articles with standard Python libraries. The actual LLM training will use fine-tuning on a model like Llama 2, hosted on AWS SageMaker. This gives you a dedicated model, not a shared API, so it learns your specific data. The interface will be a simple Flask app, also hosted on AWS, connecting to the SageMaker endpoint. Users will interact via this web interface. I'll host the entire system on AWS, using SageMaker for LLM training and inference, and EC2 or Fargate for the Flask application. This is a clean separation of concerns. Preferred Freelancer here, 5.0 over 10 reviews, no missed deadline and nothing over an agreed price yet. I need to know the format of your video files and article files, and if there are any specific types of content to prioritize during training. A short call on Freelancer would be good to settle the scope, covering data ingestion specifics and the user interaction flow.

@toqeer74

Hi, You need someone who can build an LLM trained on hundreds of videos and articles, along with an effective interactive interface for user engagement. How do you envision users interacting with the model? Here's how I'd do it: - Train the LLM using TensorFlow or PyTorch on your health and wellness content. - Host the model on a cloud platform like AWS or Google Cloud to make sure scalability. - Develop a user-friendly web interface for smooth community interaction, integrating with the model's API. I can have a working version of the core piece ready on a test link within a day. What specific features do you want in the user interface? Toqeer S

@cuyodigital

:) Hello, I'm Ricardo from Buenos Aires. :) What you have isn't really a training-volume problem, it's a knowledge-retrieval problem. Hundreds of videos and articles need to become a reliable knowledge layer that the community chatbot can search and use without forcing all source content into the model's weights. I would first collect and normalize the source material, transcribe the videos, and structure the content with metadata and source references. Then I would build a RAG pipeline with embeddings and a vector store, connect the LLM to that knowledge layer, and add the community-facing chat interface. For hosting, I would use a managed cloud deployment with the model/API layer, database and vector store separated so each component can scale independently. I would then test retrieval quality, citations and response behavior against representative questions. I would avoid fine-tuning hundreds of sources unless testing shows it provides a real advantage over RAG. Do you want the chatbot to answer only from the supplied health content, with source references in each response? I can sketch the source-to-RAG-to-chat architecture before you decide anything. Free, no commitment. If you want, I can share some examples of my previous projects here on Freelancer.com, so you can evaluate my skills and experience.

@invoketech

Hello, I have reviewed your requirements and can build a custom health and wellness AI assistant using your collection of videos and articles as the knowledge base. I have 10+ years of experience with Python, LLM integrations, RAG, embeddings, vector databases, APIs, and web application development. Rather than training a foundation model from scratch, I would recommend a RAG-based approach so the LLM can securely retrieve relevant information from your videos and articles and generate grounded responses. The system can be hosted on AWS or another suitable cloud platform, with the knowledge pipeline, vector database, LLM API, and community-facing chat interface separated for easier maintenance and scaling. Working Flow: Content Processing → Transcription & Extraction → Embeddings/Vector DB → RAG + LLM → Community Chat Interface → Testing & Deployment Please share the approximate number/length of videos, articles, and your preferred hosting environment so I can suggest the most suitable architecture and confirm the MVP scope. Thanks, Invoke Tech

@abdulhamidh2

Hi, I can build the AI system around your videos and articles and provide a practical setup for hosting and community access. Rather than retraining an LLM from scratch, I’d use a suitable existing model with a RAG pipeline, where your videos and articles are processed, indexed and retrieved to generate accurate answers based on your content. It can be hosted on a cloud server with the AI backend exposed through an API, then connected to a clean web interface where community members can interact with it. I can also handle content processing, authentication, user access and deployment. Thanks

@serdarp4

Hi, Serdar here from The Colony. You asked how it would be hosted, so I'll answer directly — but that answer depends on one correction worth making first. Hundreds of videos and articles isn't enough to train or fine-tune a model, and fine-tuning wouldn't do what you want anyway. It teaches style and format, not facts — a fine-tuned model confidently invents answers that sound like your content without being in it. What actually delivers what you're describing is retrieval: your content indexed in a vector database, relevant passages pulled at question time, an existing model answering from them. Hosting is then simple and cheap — no GPUs: - App and API on standard cloud hosting (Railway, Render or AWS). - Vector database managed — Pinecone, or Supabase pgvector to keep it in one place. - The model via API, billed per use. Plan: 1. Transcribe videos with timestamps so answers link to the exact moment. 2. Chunk and embed videos and articles into the vector store. 3. Retrieval and answering layer with source citations. 4. Chat interface for the community, with login if needed. 5. Admin view to add content and see what members are asking. 6. Deploy, document, hand over. Q1 – Where do the videos live — YouTube, or your own files? Q2 – Roughly how many community members? Q3 – Public content, or members-only? Timeline: 4–6 weeks. Budget: $4,000–8,000. Available now. Best, Serdar

@miguelcen64

Hello, I can build your AI-powered LLM platform trained on hundreds of videos and articles, with a community-friendly interface where users can interact with the AI. **Hosting Approach:** I recommend a cloud-based architecture using AWS or a similar provider. The LLM can be hosted on dedicated GPU infrastructure or through a managed LLM API, depending on your performance and budget requirements. Your videos and articles will be processed into a searchable knowledge base using RAG, allowing the AI to provide accurate, source-based answers without requiring expensive full model training. I’ll develop a modern chat interface, user authentication, community integration, conversation history, and scalable backend APIs. With my full-stack and AI development experience, I can deliver a secure, responsive, and production-ready solution tailored to your community. Let’s discuss your content and hosting preferences.

@FizzaNadeemK

I can build this as a knowledge based AI system using your videos and articles as the source material, with a RAG architecture rather than trying to retrain an LLM from scratch. The system can be hosted securely on AWS or another cloud platform, with your content processed into a searchable knowledge base and the AI generating answers from that information. I can also build a clean web interface where your community can chat with the AI, with user authentication, conversation history and usage controls if needed. I will handle the full setup from content processing and retrieval to the backend, hosting and frontend interface, making the system easy to maintain and expand as you add more content.

@ludiac

Hi, I’d build this as a RAG-based LLM system rather than training a model from scratch. The videos and articles would be transcribed/extracted, chunked, embedded, and stored in a vector database. The LLM then retrieves the relevant knowledge and generates grounded responses. Hosting: I’d recommend AWS for production, with the application/API hosted on AWS and the LLM accessed through a managed model API or deployed model endpoint depending on your privacy and cost requirements. The knowledge base can use S3 + PostgreSQL/pgvector or a dedicated vector database. Interface: I’d build a clean web chat interface where community members can log in and interact with the AI. We can also include source references so users can see which videos/articles informed each answer. Timeline: 3–5 weeks This approach is scalable, easier to update when new content is added, and avoids the cost and complexity of retraining the model every time your library changes. Thanks

@gopalkrgautam

A chatbot answering from your health and wellness content is doable at this budget — fully retraining an LLM isn't, and your community won't see the difference. Hosting: a small server you own, no monthly API bills. I'd transcribe the videos, ingest the articles, and build a chat interface where users ask questions and get answers grounded in your material. Demo in 3 days, full version within a week. NEOS, an agentic platform I built, runs this exact chat-over-your-content setup on a Raspberry Pi — self-hosting is already proven. Send me five articles and one transcript and I'll have a working demo pulling real answers within 48 hours. One question: are the videos coming as transcripts, YouTube links, or raw files?

@wilmerae

Thank you so much for sharing the details. For hundreds of videos and articles, I’d first determine whether you actually need to train a model from scratch or whether a RAG-based system would give you a better result with lower cost and easier updates. I can build the pipeline to process your videos/articles, make the knowledge searchable, and connect it to an LLM so users can interact with it through a clean community-facing interface. For hosting, I’d recommend starting with a cloud VPS or AWS depending on expected traffic. The LLM/API, knowledge base, and web interface can be hosted separately so the system can scale without rebuilding it later. If you can share the type of content and expected number of users, I can suggest the right architecture and hosting cost before development.