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You will get Enterprise Self hosted AI platform, integrations, agents fine-tuned models

Vahit U.Status: Offline
Vahit U. Vahit U.
5.0
Top Rated
Preview image

Let a pro handle the details

Buy Generative AI services from Vahit, priced and ready to go.
Vahit U.Status: Offline
Vahit U. Vahit U.
5.0
Top Rated
Preview image

Let a pro handle the details

Buy Generative AI services from Vahit, priced and ready to go.

Project details

You will get a production-ready Sovereign AI SaaS MVP that connects your private data sources, custom AI agents, RAG knowledge layer, workflow automations, and deployment foundation into one usable platform.

This is not a simple chatbot or demo automation. I design the system as a real AI product: secure data connections, agent tools, API/database integrations, custom workflows, clean web interface, model/provider integration, and deployment-ready backend architecture.

The platform can connect to documents, databases, APIs, CRMs, internal tools, and cloud apps, then use AI agents to search knowledge, automate tasks, summarize information, trigger workflows, and support business operations.

My background combines AI architecture, RAG systems, LangGraph/LangChain agents, FastAPI backends, vector search, database integrations, Docker/Kubernetes deployment, and production AI infrastructure. The result is a modular and scalable AI SaaS foundation that you can extend after the MVP.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer Model
AI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI-Generated Video, AIOps, Conversational AI, Natural Language Generation, Synthetic Data Generation
AI Development Language
Python
AI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Jasper AI, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow
AI Models
ChatGPT, LLaMA, OpenAI Codex

What's included $2,500

These options are included with the project scope.

$2,500
  • Delivery Time 7 days
  • Number of Revisions 3
    • AI Model Integration
    • Database Integration
    • Detailed Code Comments
    • MLOps
    • Model Deployment
    • Model Documentation
    • Model Monitoring
    • Model Testing & Optimization
    • Model Tuning
    • Pre-Training
    • Prompt Engineering
    • Source Code
Optional add-ons You can add these on the next page.
Additional Revision
+$125

Frequently asked questions

5.0
3 reviews
100% Complete
(3)
1% Complete
(0)
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FA

Fahad A.
5.00
Jun 15, 2026
Local LLM Consultation for 25-30 users

YC

Yasemin C.
5.00
Dec 6, 2025
We are looking for AI professionals for a usability test!

JS

James S.
5.00
Oct 12, 2025
Senior AI Architect: Constitutional Cost Control & Multi-Agent Orchestration He seems very knowledgeable, competent and honest. A rare find.
Vahit U.Status: Offline

About Vahit

Vahit U.Status: Offline
AI Architect | AI Engineer | Consultant | Product Owner
100% Job Success
5.0  (3 reviews)
Rotterdam, Netherlands - 8:17 am local time
I architect and deploy production-grade AI systems — from LLM inference infrastructure to multi-agent workflows and RAG pipelines. My work spans the full stack: GPU cluster management, model deployment, agentic system design, and enterprise integration.
💡 Notable Projects
mdgpt — Sovereign AI Platform (Mijndomein)
Designed and deployed a sovereign LLM inference platform for a Dutch enterprise with 1.4M+ active subscriptions. Multi-provider gateway (vLLM, Anthropic, Gemini), 4×A100 GPU cluster, Kubernetes orchestration, and 130+ production agentic tools across engineering and office teams. Stack: FastAPI, async Python, Redis, Phoenix/OTel, Prometheus, OpenWebUI.
Savion Clinic & Patient (LMXAI)
Built a two-sided clinical AI platform from scratch — a patient nutrition coaching app and a clinic assistant automating 100% of nutritionist workflows. LangGraph-orchestrated multi-agent architecture, RAG pipelines for clinical knowledge retrieval, MCP tool integration, Django + MongoDB + Redis.
LMXAI Benchmark (Context Engineering)
Achieved top-5 global ranking in LLM education benchmarks, raising accuracy from 68% → 85% through advanced context engineering, prompt optimization, and agentic reasoning within a custom evaluation framework.
🎯 How I Work
I build systems that are modular, observable, and production-ready — not prototypes. Focus is always on reliability, latency, and measurable business impact.
🧰 Core Stack
LangGraph · LangChain · FastAPI · Python (async) · RAG · vLLM · SGLang · LoRA/QLoRA · Redis · Elasticsearch · PostgreSQL · MongoDB · Docker · Kubernetes · OpenAI / Claude / Open-Source Models · MCP · Context Engineering · GPU Inference · AWQ/FP8 Quantization

Steps for completing your project

After purchasing the project, send requirements so Vahit can start the project.

Delivery time starts when Vahit receives requirements from you.

Vahit works on your project following the steps below.

Revisions may occur after the delivery date.

Discovery & scope confirmation

I review your goals, data sources, integrations, and security needs, then define the MVP scope, architecture, and delivery plan.

Architecture & integration setup

I set up the data connections, APIs, databases, RAG/knowledge layer, and agent/tool structure based on the approved plan.

Review the work, release payment, and leave feedback to Vahit.