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I will build local llm and rag with ollama
Melih Balkan
About this gig
Run a private local LLM on hardware you control without sending prompts, documents, or sensitive data to external model APIs.
I build local AI systems with Ollama, llama.cpp, RAG, vector databases, and custom agent workflows for businesses and teams who need privacy.
WHAT I CAN DEPLOY
- Local LLM setup optimized for your CPU/GPU
- Private RAG chatbot for PDFs, DOCX, TXT, CSV, and knowledge bases
- Local AI assistant with document search and source-aware answers
- Custom AI agent/workflow for repetitive internal tasks
- FastAPI/API integration, local UI, Docker, and documentation as needed
GOOD FIT FOR
Internal knowledge bases, confidential documents, legal/finance operations, private research, offline assistants, and teams avoiding recurring cloud AI API costs.
Your system can be configured to run inference and document retrieval entirely on your machine or private network.
Message me before ordering with your OS, CPU, GPU/VRAM, RAM, and desired use case so I can confirm the right model and package.
Get to know Melih Balkan
Melih Balkan
Local AI Developer
- FromTurkey
- Member sinceAug 2026
- Avg. response time1 hour
Languages
English, Turkish, Spanish
I build private local AI systems, RAG pipelines, and automation using Python, FastAPI, Ollama, llama.cpp, and vector databases. I help businesses deploy LLMs on hardware they control, build document search assistants, and automate defined workflows.
I have worked with 30+ clients across AI and automation over the last 4 years. I scope each project around your hardware, data, privacy requirements, and goals, with clear updates and documented handover.
FAQ
Do I need a powerful computer to run this?
It depends on the model and workload. A dedicated NVIDIA GPU with sufficient VRAM is recommended for faster inference, but smaller models can also run on CPUs and Apple Silicon Macs. Send me your CPU, GPU/VRAM, RAM, and use case and I can recommend a realistic configuration.
Does this require an active internet connection to work?
No internet connection is required for the core local system after the required models and dependencies are installed. Optional external integrations may require network access.
Will my prompts and documents be sent to an external AI API?
The deployment can be configured so inference and document retrieval run on your own machine or private network without sending prompts or documents to external model APIs.
What documents can the RAG system use?
Typical sources include PDF, DOCX, TXT, CSV, and structured knowledge-base content. Ask first if you have scanned PDFs, unusual formats, or a large existing database.
Do I receive the source code and setup files?
Yes, for custom code created for your project. Third-party software and models remain subject to their own licenses. I also include the agreed configuration and handover documentation.
Can you make it fully air-gapped?
Yes, when the selected software, model files, and your environment support an air-gapped deployment. Tell me this requirement before ordering so the scope includes offline dependency handling.
Can you install it on Windows, Linux, macOS, or a server?
Yes, depending on your hardware and the selected stack. Apple Silicon, NVIDIA GPU systems, CPU-only machines, and VPS/server setups can all be supported with different model choices.
Is model fine-tuning or training included?
No, not by default. This Gig focuses on deployment, RAG, integration, and local agent workflows. Fine-tuning is a separate scope and should be discussed before ordering.
How do you deploy it on my hardware?
I provide the project files and deployment steps and can guide the setup through Fiverr communication. The exact method depends on your environment and access constraints.
What should I send before ordering?
Your OS, CPU, GPU model and VRAM, RAM, available storage, target use case, document/data types, preferred interface, and whether you require offline or air-gapped operation.
