Build a Secure Local AI System (LLM, RAG & AI Agents)

Budget: 2000 UAH Deadline: 1 day

Hello!
My name is Oleg, I am the project manager at Business Atlas Automation (BAA). We are official partners of n8n and develop private Production AI systems, RAG pipelines, and multi-agent ecosystems with strict data security requirements.
Our technical expertise and stack:
Local LLM & VPS: We deploy and optimize open-source models (Llama 3, Mistral, DeepSeek) via Ollama in Docker containers on a private server environment (Hetzner, AWS).
RAG & Vector Databases: We build RAG architecture on LlamaIndex / LangChain with vector stores (Qdrant, ChromaDB, Pinecone), implementing indexing, deduplication, and versioning with mandatory source citation.
OCR & Document Processing: We create pipelines for parsing unstructured PDFs, DOCX, XLSX, and meeting transcripts (Whisper + LlamaIndex).
Backend and Security: We develop APIs in Python (FastAPI), configure data isolation, and role-based access control.
Relevant experience in the portfolio:
1. Smobile (Private AI Pipeline): Self-hosted infrastructure (n8n + Ollama + ChromaDB) for processing a catalog of 1700+ SKUs while ensuring 100% confidentiality.
2. BikeNow Support Agent: RAG system based on the company's local knowledge, automatically handling over 41% of inquiries with source references.
3. Multi-agent system (Python/Redis): A team of 4 specialized AI agents for processing, analyzing, and summarizing large volumes of information.
Message me privately to discuss the details. Expand

Budget: 10000 UAH Deadline: 15 days

Hello! I have experience with AI agents, OpenAI API, VPS deployment, and business process automation.

For this project, I can implement a document processing pipeline with AI agents for meeting transcriptions, action item extraction, and company knowledge base. VPS deployment and n8n as an orchestrator for process automation.

Portfolio: https://interesting-galley-0eb.notion.site/Portfolio-ff53a82144bf8251bc1581a6cef25f7d

What is the priority feature for the MVP and what volume of documents do you plan to index?

Budget: 12000 UAH Deadline: 6 days

Read through the brief: a self-hosted platform combining a local LLM, RAG with cited sources over company documents, and agents for meeting transcripts, summaries, action items and client knowledge retrieval, with strict data isolation between clients.

I have shipped a production RAG system before: a voice AI assistant for veterinary clinics, FastAPI in front of the retrieval and orchestration layer, Qdrant as the vector store, and an LLM doing tool-calling into the CRM live during calls, currently serving real clinics. The multi-format ingestion side here, PDF with OCR, DOCX, XLSX, meeting transcripts, with dedup and versioning, is new territory in this exact combination, but it is the same shape of problem underneath: normalize input, chunk, embed, index, retrieve with citations. I would build it the same disciplined way, self-hosted end to end, FastAPI for the API layer, Docker for deployment on your VPS or dedicated server, and a local model runtime sized to whatever hardware you have.

For the agent layer I would keep transcription processing, summarization and action item extraction as separate, independently testable steps rather than one large prompt chain, so failures are traceable and each piece can be tuned without touching the others. Client data isolation I would enforce at the retrieval layer itself, not just API access control, separate collections or namespaces per client so a query cannot cross tenant boundaries even by mistake.

Two things that change the actual build: what hardware is available for the model, GPU and VRAM or CPU only, and roughly how many documents and clients you are indexing at launch versus growing into. The listed budget does not match this scope, happy to start with a scoped first stage, ingestion plus RAG with citations, deployed and testable on real questions, and price the agent layer once that is running.

Budget: 27000 UAH Deadline: 15 days

Hello. I can implement this project. If it's relevant, write to me, and we will discuss.

Cost - $3000 for the project.

Budget: 700 UAH Deadline: 7 days

Good day, Anastasia!

I have experience in creating secure AI platforms that integrate local LLMs, RAG, and AI agents for automating business processes.

I understand that you need a reliable AI Automation Engineer to develop a platform that can safely index company knowledge, process meeting transcriptions, and automate internal processes. This is an important task, and I am ready to offer my expertise to achieve your goals.

To develop a specific strategy, it is necessary to study your requirements and objectives. I suggest we first discuss the project details to understand which functions and technologies will be best for your platform.

My preliminary estimate for my services is from $400 per month. Depending on the complexity of the project, timelines may vary from 2 to 8 months.

My portfolio includes examples of similar projects that demonstrate my approach to creating AI systems. I invite you to take a look at them.

I propose we move to discussing your project in private messages to thoroughly consider your needs and action plan. Expand

Budget: 700 UAH Deadline: 3 days

Hello. Have you already decided on a specific open-source LLM for local deployment, or do you plan to choose it at the start of the project? I will clarify the timelines and budget in personal correspondence.

Here’s how I will execute this project:
1. I will deploy a local LLM on a VPS using Docker and optimize it for your tasks.
2. I will build a RAG pipeline on LlamaIndex with a Qdrant vector database, indexing PDF, DOCX, TXT, XLSX, and OCR.
3. I will create AI agents for processing meeting transcriptions, extracting actions, and knowledge search, as well as a FastAPI interface with access control.

Thank you for considering my proposal. I look forward to the opportunity to collaborate with you!

Budget: 700 UAH Deadline: 1 day

Hello Anastasia,

This is close to what I already build. My desktop AI assistant runs a hand-written agent loop and a RAG pipeline I wrote from scratch — chunking, embeddings, hybrid keyword + semantic scoring — no LangChain. It works with local models via Ollama as well as hosted APIs, so a fully self-hosted setup is the default case, not an adaptation.

Before quoting the full system, I need to understand the constraints:

What hardware will host the model — GPU available, how much VRAM? This decides which model is realistic.
Volume and format of the knowledge base — how many documents, PDF/DOCX/email/wiki?
How many users, and does it need role-based access to documents?
Meeting transcripts — do you already have them as text, or is transcription part of the scope?
Which internal processes should the agents automate — could you name two concrete ones?

My proposal: start with Stage 1 — a working self-hosted RAG over your documents with source citations, deployed on your infrastructure. 12,000 UAH, 6 working days. You get a running system you can test on real questions before committing to the rest.

Agents and transcript processing are Stage 2, priced once Stage 1 shows how your data actually behaves.

The 700 UAH budget doesn't match the described scope — I assume it's a placeholder. If the real budget is fixed and much lower, tell me now and we'll both save time.

Code: github.com/kamedashe Expand