You will get an LLM deployed inside your own network, with nothing leaving it
Project details
Your data cannot go to OpenAI. Legal said no, or the contract said no, or the regulator will. So every AI vendor you have talked to is selling you something you are not allowed to buy.
I put the model inside your network instead.
An open model - Llama, Mistral, Qwen - chosen for the hardware you actually own, deployed on your server, exposed as an OpenAI-compatible endpoint your existing code can call with a one-line change. No API key. No egress. Nothing to explain to your security team.
For two years I have run AI systems inside a Fortune 500 estate, deployed across 17+ environments with no internet egress, with retrieval over 34,369 indexed documents. On my own hardware I run llama.cpp and a GPU inference server in production. Underneath that, 15 years of Java and Spring Boot in enterprise production.
I will also tell you when self-hosting is the wrong answer. If your workload is small and your data is not sensitive, an API is cheaper and I will say so before you spend anything.
Best fit: health, legal, finance, defence-adjacent, or any team with a data-residency clause and a model they are not permitted to send anything to.
I put the model inside your network instead.
An open model - Llama, Mistral, Qwen - chosen for the hardware you actually own, deployed on your server, exposed as an OpenAI-compatible endpoint your existing code can call with a one-line change. No API key. No egress. Nothing to explain to your security team.
For two years I have run AI systems inside a Fortune 500 estate, deployed across 17+ environments with no internet egress, with retrieval over 34,369 indexed documents. On my own hardware I run llama.cpp and a GPU inference server in production. Underneath that, 15 years of Java and Spring Boot in enterprise production.
I will also tell you when self-hosting is the wrong answer. If your workload is small and your data is not sensitive, an API is cheaper and I will say so before you spend anything.
Best fit: health, legal, finance, defence-adjacent, or any team with a data-residency clause and a model they are not permitted to send anything to.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
LLaMA, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$1,400
|
Standard
$3,200
|
Advanced
$6,500
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 30 days |
Number of Revisions | 1 | 1 | 2 |
AI Model Integration | - | ||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | |||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | ||
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | |||
Source Code | - | - | - |
Frequently asked questions
28 reviews
(28)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
RS
Robert S.
Oct 9, 2023
Senior Java Engineer
Solid Java engineer!
BB
Bassel B.
May 5, 2023
Senior/expert Java developer needed for APIs development
Good developer
RW
Rebecca W.
Apr 4, 2023
Python Developer
Thank you for all your hard work in making this project a success.
RW
Rebecca W.
Dec 4, 2022
Python Developer
WA
Walter A.
Oct 13, 2022
Experienced Java Developer
These reviews can sometimes be vague, so let me be absolutely clear: Moshiour is an exceptionally talented backend developer. He rose to a leadership level fairly shortly after beginning work with us. The project he inherited was challenging across a number of dimensions including having been developed using an ancient version of Java which he stepped in and became not only responsible for maintaining as a real time production application, but also for adding and adjusting features without rewriting the application. Eventually, the client sunset the project, but Moshiour remained until the end as his expertise was needed. Highly recommended!
About Moshiour
LLM Engineer | Multi-Agent Systems & RAG | Fortune 500 + SaaS Founder
Dhaka, Bangladesh - 2:39 am local time
Flagship client: Princess Cruises (Carnival Corp, Fortune 500) — sole AI architect across 17+ deployed ship environments.
Platform 1: Multi-agent BDI operations assistant. Custom orchestration written from scratch — no framework wrappers. LLM planning, guardrails, human-in-the-loop approvals, self-hosted Ollama at the ship edge. Fully air-gap compatible.
Platform 2: Fleet-wide agentic assistant. Hybrid-retrieval RAG with 34,369 Confluence pages indexed into ChromaDB. MCP tooling invoking 40+ microservices in real time.
15+ years of Java and Spring Boot across 40+ microservices in enterprise production. 100+ P1 incidents resolved — I know what breaks at scale and why.
Most AI engineers are Python-only and treat your existing backend as a black box.
I architect AI directly into enterprise Java infrastructure — agent tooling, RAG pipelines, and LLM orchestration integrated into the systems you already run.
$400K+ earned on Upwork. Top Rated. 12,755 hours logged.
I also run TechyOwls, my own AI SaaS studio — because I prefer shipping over advising.
SnapForge — screenshot and agentic data extraction API. LangGraph-based. Live with paying customers.
Isolate — AI image processing platform. Background removal, upscaling, virtual try-on, local vision LLMs.
LLM Bridge — WireGuard-tunneled inference between VPS and home GPU. Enterprise-grade inference without cloud lock-in.
Stack: LangGraph, LangChain, MCP, ReAct, BDI multi-agent, ChromaDB, pgvector, self-hosted Ollama, Llama 3.x Vision, Claude, OpenAI. Java, Spring Boot, Python, FastAPI, TypeScript. Docker, Kubernetes, Ansible, Prometheus, Grafana. Oracle, PostgreSQL, Redis, Kafka.
WHAT YOU GET
When you bring me in, you get someone who has already made the architecture decisions you're about to make — at Fortune 500 scale, under real compliance pressure, in production environments where failure costs money.
I embed as technical lead alongside your existing team or execute independently — both at the same standard. System design, agent orchestration, RAG pipeline, security review, stakeholder communication. You focus on the product. I keep the AI from becoming a liability.
WHERE I ADD THE MOST LEVERAGE
Teams past the prototype stage that need production-grade AI — systems that survive security review, scale to real users, and run in regulated or air-gapped environments. Enterprise codebases where AI needs to integrate with existing Java/microservices infrastructure,
not replace it.
Currently accepting a limited number of long-term engagements. Fixed-scope architecture audits also available.
Steps for completing your project
After purchasing the project, send requirements so Moshiour can start the project.
Delivery time starts when Moshiour receives requirements from you.
Moshiour works on your project following the steps below.
Revisions may occur after the delivery date.
Hardware and workload review
What you own, what you need, and an honest answer on whether self-hosting is right for this workload.
Model selection and benchmark on your machine
Two or three candidate open models benchmarked on your hardware for quality, throughput and latency. You see the numbers, not a leaderboard.