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

Budget: 27000 UAH Deadline: 40 days

Hello. I will design a secure isolated AI ecosystem based on a local LLM (Llama/Mistral) and a RAG pipeline to automate your business processes. I will build the core architecture using Python (FastAPI + LlamaIndex), and I will configure vector search with strict source citation through Qdrant to completely eliminate hallucinations. I have extensive experience in developing high-load architectures with strict data isolation (I have created complex trading MAM terminals and Web3 trackers with instant data pool processing). I will isolate all indexing modules, OCR processing of PDF/DOCX, and AI agents for transcript analysis in Docker containers and deploy them on your Linux VPS.

Budget: 27000 UAH Deadline: 10 days

Hello!
I am Oleg, project manager at Business Atlas (official partners of n8n). Our team specializes in the development and deployment of local AI systems, RAG architectures, and private multi-agent ecosystems (50+ completed projects).
We will build for you a fully autonomous, secure, and self-hosted AI platform with local LLMs, a RAG pipeline, and agents for processing documents and transcripts.
1. Proposed stack and architecture
Local core (Local LLM): Deployment of DeepSeek / Llama 3 via Ollama / vLLM in Docker containers on a dedicated VPS (full privacy).
RAG & Vector DB: LlamaIndex + Qdrant / Supabase (pgvector) with metadata management, deduplication, versioning, and strict source citation.
OCR & Document Parsing: Automated pipelines for processing PDF, DOCX, XLSX, TXT, and meeting transcripts.
Agents and API: FastAPI microservices + self-hosted n8n for orchestrating agents (meeting summaries, extracting Action Items, client knowledge).
Security and isolation: Strict access control (RBAC), authorization, SSL/WireGuard for port protection.
1. Our experience and portfolio
Deployment of a local AI pipeline in Docker based on Ollama (Llama 3/Mistral) for secure processing of internal data.
RAG pipeline and vector layer (3000+ SKUs) based on Qdrant for semantic search.
Intelligent parsing of primary documentation, PDF extracts, and medical orders.
Assistants for automatic note-taking of broadcasts, extracting tasks, and synchronizing with CRM.
1. Stages, timelines, and costs
Discovery & Infra (specifications, VPS, Docker, n8n, Local LLM): 7-10 days — $600 - $900.
RAG & Doc Pipeline (indexing PDF/XLSX, OCR, source citation): 14-20 days — $2,000 - $3,500.
Agentic Workflow (meeting agents, extracting actions, FastAPI/Web UI): 14-20 days — $2,000 - $3,500.
Total: 7-9 weeks | $4,600 - $7,900 + 14 days of technical support.
Message me privately — let's discuss the details to get started! Expand

Budget: 10000 UAH Deadline: 7 days

Hello, Anastasia!

I can take on the first stage: to design and deploy the basic core of a self-hosted AI system with a local LLM, RAG search through documents, and responses with links to sources.

My profile is close to this task: I work with Python, Linux, Docker, local AI inference, ONNX Runtime, LLM integrations, STT/TTS pipelines, and embedded/edge AI systems. I developed an AI voice assistant platform with wake-word detection, VAD, speech-to-text, text-to-speech, local LLM inference, and deployment on Linux devices. Therefore, I understand not only the API level but also practical issues of deployment, performance, stability, and model operation in a controlled environment.

Here’s how I would start:

1. I would select a realistic stack for your VPS/server: Ollama / llama.cpp / vLLM for local LLM, Python + FastAPI for backend, Qdrant or PostgreSQL + pgvector for vector search, Docker for deployment.

2. I would build the RAG core: document loading, chunking, embeddings, metadata, deduplication, indexing, searching for relevant fragments, and generating responses based only on the found sources.

3. I would add citation-based responses so that the system not only answers but also shows which document the information is taken from. If the answer is not in the database, the model should honestly report this.

4. I would lay out the architecture for the next stages: OCR for PDF, DOCX/XLSX/TXT, meeting transcripts, meeting summaries, task extraction, access control, query history, and client data isolation.

I suggest not building the entire platform blindly at once, but starting with the first controlled stage: RAG core + local LLM + testing on your real documents. After this, it will be clear which model fits, what the server requirements are, how to best build the agent layer, and how long the complete system will take.

Estimated time for the first stage: 7 days.
Cost for the first stage: 10,000 UAH.

After the first stage, I will be able to provide an accurate estimate for the full implementation by milestones. Expand

Budget: 12000 UAH Deadline: 7 days

Hi Anastasia,

This is exactly the kind of system I build day to day — I run a small AI studio where I've shipped and operate production RAG/agent platforms myself (not API wrappers): a self-hosted knowledge-Q&A product with cited-source answers over a live document base, plus an internal agent-orchestration layer for automating workflows. Stack overlaps closely with what you listed: FastAPI, Docker, Qdrant/FAISS, LlamaIndex-style ingestion with OCR for PDFs, and local/open-source LLM deployment on a VPS.

For a system like this I'd start with a scoped first milestone rather than committing to the full platform blind: stand up the core RAG pipeline (document ingestion incl. OCR, indexing/dedup, vector store) on a sample of your real documents, wired to a local LLM with source-cited answers you can test end-to-end. Once that's validated, we layer in the meeting-transcript agent, summaries/action-item extraction, access control and the query interface as separate milestones — each with its own deliverable and check-in, paid through FH Safe.

One question before I size the later milestones: roughly what document volume / number of users does the system need to support, and do you have a target budget range in mind for the full build? That determines whether the local LLM runs comfortably on a single VPS or needs GPU-backed hosting, which changes both architecture and cost.

Happy to share examples of the RAG/agent products I've built once we're talking specifics.

Best,
Stas Expand

Budget: 27000 UAH Deadline: 7 days

Hello. I have experience with RAG,Python,Langchain,etc.I'm ready to cooperate!

Budget: 18000 UAH Deadline: 14 days

Hi! I've built self-hosted RAG systems in Python — local LLMs (Llama/Mistral via Ollama/vLLM) behind FastAPI, with LlamaIndex ingestion and Qdrant for vectors, all dockerised on a Linux VPS. For meeting transcripts and action-item extraction I run a small agent layer on top of the same retrieval, with citations pulled straight from the source chunks to keep hallucinations down. One thing that shapes the whole architecture: roughly how many documents and users are we indexing, and do you need hard data isolation between clients (separate collections/tenants) or is a single shared index fine? I'd start with the ingestion + RAG core and grow from there. Around 14 days.

Budget: 18000 UAH Deadline: 14 days

Hello Anastasiia,

I can build the first production-ready stage of this system: private document ingestion, local LLM deployment, RAG with source citations, basic access control, and a Dockerized API on your server.

Relevant work:
• youtube_helper: a Telegram RAG assistant with FAISS, Gemini embeddings, hybrid vector + keyword retrieval, MMR context selection, and scheduled ingestion.
• lexai: a Ukrainian legal AI assistant with grounded search over court decisions.
• edrsr-ai-server: a Node/Postgres queue and worker platform for asynchronous document analysis.
• fasttravel: a Docker-based production stack with CI/CD, monitoring, backups, and security checks.

I would start with a real document sample and an evaluation set. The pipeline would normalize PDF, DOCX, TXT, XLSX, OCR output, metadata, versions, and duplicates before indexing. Answers would require retrieved evidence and return source references. Tenant boundaries and authorization would be designed before adding agents.

My bid is 18,000 UAH and 14 days for Phase 1: deployment, ingestion for the listed formats, Qdrant or pgvector, cited RAG, FastAPI endpoint or simple web UI, Docker setup, tests, and documentation. Meeting-summary and action-item agents can then be added as a separately accepted milestone.

The key sizing question is the expected corpus volume, concurrent users, and available CPU/GPU/RAM. That determines whether Ollama/llama.cpp is enough or whether vLLM on a GPU server is justified. Expand

Budget: 5000 UAH Deadline: 7 days

Good day. I work on this every day: I have my own multi-agent AI system in production — 100,000+ lines of Python (FastAPI), 750+ API endpoints, 1,000+ automated tests.
What is implemented in it (all — custom code, I will show it live):
Orchestrator: 19 specialized agents, tasks as a dependency graph — parallel waves.
Agents: Planner → Specialist → Reviewer → Critic → Finalizer.
Model router: model selection based on task complexity, multi-provider support (OpenAI-compatible APIs, Gemini, Groq, DeepSeek) with automatic fallback, accounting for quotas and model success. Integrated Telegram Discord bots.
I am ready to show the system live.

Budget: 9000 UAH Deadline: 7 days

Hello!

I am ready to take on the first stage: deploying the core of a self-hosted AI system — local LLM + RAG search through documents with cited sources.

My stack is Python + FastAPI, and I have a completed project Carshering-FastAPI (https://github.com/idelzss/Carshering-FastAPI) with backend, authorization, and database work. I also have practical experience with LLM pipelines in production: in instagram_post_proj (https://github.com/idelzss/instagram_post_proj), I integrated several providers (Groq, DeepSeek, Gemini) with request routing and structured prompt generation — so I know from experience how to keep such a pipeline stable.

Plan for the first stage:

1. Selecting the stack for your VPS: local LLM (Ollama/llama.cpp) + FastAPI + vector database (Qdrant or pgvector).
2. Basic RAG outline: loading documents → chunking → embeddings → searching for relevant context → generating a response strictly based on it.
3. Citing sources in responses, with an honest "not in the database" if no context is found.
4. Architecture with a margin — so that OCR, transcripts, agent layer, and access control can be added on top without reworking the core.

I suggest testing the approach immediately on your real documents, rather than designing the system in advance in a vacuum — this way, it will be clear what model and infrastructure are actually needed.

Deadline: 7 days. Cost: 9,000 UAH.

After this, I will provide an accurate estimate for the remaining milestones. I am ready to show the code of both projects — when is it convenient to talk? Expand

Budget: 10000 UAH Deadline: 14 days

I will build the system for you step by step: first, the RAG core (PDF ingestion with OCR, DOCX, XLSX, deduplication, Qdrant, responses with citations through a local model on Ollama or vLLM), then agents for transcripts (summaries, task extraction), and finally FastAPI with access control and client data isolation. Each stage will be delivered and tested separately on your real documents through Safe.

You can poke my RAG in production right now: find the bot auth_ai_radar_bot on Telegram. Hybrid search (semantics + BM25 with a reranker), the bot responds only based on its database with a reference to the source, and when there are no answers in the database, it honestly declines instead of hallucinating. The same pattern will form the basis of your knowledge base. An example of the agent layer is also live: research-agent on HuggingFace Spaces, profile BlankD1 (LangGraph, tool use).

Stack for your list: Python, FastAPI, Docker, Qdrant, LlamaIndex or LangChain, Llama/Mistral/Qwen locally, Linux VPS.

We can discuss all the details regarding the volume of documents, server, and database language in the chat.

Budget: 15000 UAH Deadline: 12 days

Good day, Anastasia! I will deploy a local LLM (Llama/Mistral/Qwen) on a VPS via Docker, build a RAG pipeline (LlamaIndex/LangChain + vector database like Qdrant/Chroma) with support for PDF/DOCX/TXT and meeting transcripts, with clear isolation of client data. I will add agents for processing transcripts, summarizing meetings, and extracting actions, an API or a simple web interface for queries, responses with source citations to minimize hallucinations. Please clarify the estimated budget for VPS/hosting - this will determine which models can realistically be run locally.

Budget: 1166 UAH Deadline: 10 days

I run a production RAG system with sub-80ms latency, partly by skipping MCP-tool overhead for the hot path - hybrid retrieval runs through a dedicated in-service HTTPS port with models wired directly into it.
Memory layer: 5 layers (2 cognitive, 1 graph-based) plus a background daemon handling embedding, ranking, NER, and live SQL-to-vector sync. Local LLM hosting: 3 models currently in production, ~8 tested over time - selection based on real comparison, not a default pick.

Also: close to 2 years as an AI trainer/reviewer for Scale AI and Meta AI across multiple NDA projects - evaluating model outputs at a level most developers never see, which directly feeds into how I design source-citation logic to minimize hallucinations.

Production deploys run on Hetzner (Docker + Coolify), including one handling full billing + security logging via Supabase auth across ~15 tables - directly relevant to your data isolation requirement. Meeting transcripts -> summaries -> action items fits naturally on top - I already have 10-15 agent tools in production for this kind of task.

One thing to align on early: your VPS/host budget caps available RAM/VRAM, which decides which open-weight models are viable locally and shapes the whole architecture. This only becomes a non-issue with an unlimited budget. So the real starting point: what's the budget range, and what hardware specs are locked in?

Rate: €22/hr - timeline follows once the above is clear.
The established price and terms are determined due to platform requirements and require discussion.

Happy to walk through the architecture in a personal chat. Expand

Budget: 20000 UAH Deadline: 14 days

Hello! I am very interested in your project to create a private AI ecosystem.

My key advantage for this task is that I am not only a Python developer working with LLM, but I also have a strong background in Linux server administration. This means I understand how to properly isolate the environment, set up Docker, and ensure the secure deployment of local models (Llama, Mistral, Qwen) on your VPS without the risk of data leakage.

My practical experience:
In my recent project (autonomous trading bot), I implemented a complex architecture that includes seamless switching between cloud APIs and locally deployed LLM.

How I envision the implementation of your RAG pipeline:

LLM Core: Local deployment of an optimized model on Linux VPS (Docker + FastAPI).

RAG & Vector DB: Using LlamaIndex + Qdrant (or Chroma) for fast semantic search across indexed PDF/DOCX.

AI Agents: Creating separate workers for parsing meeting transcripts and extracting specific actions with clear references to sources (to reduce model hallucinations to zero).

You can review the quality of my code and architectural solutions on my GitHub: https://github.com/hvoi/Testnet-binance-vision-AI

Since the project is large, I suggest breaking it down into stages (sprints). I am ready to discuss the details of the first stage (for example, deploying the basic RAG for documents) and get started. Expand

Budget: 12000 UAH Deadline: 7 days

Hi. I read the brief carefully. You need a self-hosted AI stack: a local LLM on your own VPS, a RAG pipeline with source-cited answers, document ingestion for PDF with OCR, DOCX, XLSX and meeting transcripts, plus agents for summaries and action-item extraction, all behind strict data isolation and access control.

This is close to what I build day to day. On a released product I put together a full RAG pipeline over Qdrant with an LLM as orchestrator and citation-grounded answers to keep hallucinations down, so the ingestion, chunking, metadata and dedup layer is familiar territory. For the serving side I would run an open model (Llama, Mistral or Qwen class) on the server, expose it through a FastAPI service, keep everything in Docker, and put per-client isolation at the storage and retrieval layer so one tenant can never read another tenant's index.

For the agents part I would keep them as small, well-scoped tools (transcript intake, summary, action items, knowledge retrieval) rather than one big prompt, which is more reliable and easier to test.

A couple of things that decide the architecture: roughly how many documents and what total volume do you expect in the first months, and do you already have a target server (GPU or CPU-only), or should I size the model to your hardware? Happy to walk through the deployment and hardening plan on a call.