You will get AI Architecture Audit | Production LLM System Design & Review
Project details
Most AI systems don’t fail in demos.
They fail in production.
They break when exposed to real users, real data & real scale
*Retrieval returns irrelevant results
*Latency spikes under load
*Context windows overflow
*Costs grow uncontrollably
The problem isn’t prompting.
It’s architecture.
I audit your LLM or RAG system and show you exactly
*what’s breaking
*why it’s happening
*& how to fix it
This is not generic advice.
You get a clear, production focused architecture review with actionable fixes.
What I analyze
*Retrieval accuracy and ranking quality
*Latency and system bottlenecks
*Token usage and cost inefficiencies
*Prompt and context design issues
*Overall system architecture & scalability
What you get
*12–20 page architecture audit report
*Prioritized, actionable recommendations
*Clear next steps for optimization
Why me?
13 years of enterprise engineering experience at
*IBM
*Bank of America
*TIAA
Built AI systems processing 10,000+ documents per day on Azure OpenAI in production.
I don’t just design systems. I build ones that actually work.
Send me a brief about your system.
I’ll tell you straight if this audit will help, before you spend anything.
They fail in production.
They break when exposed to real users, real data & real scale
*Retrieval returns irrelevant results
*Latency spikes under load
*Context windows overflow
*Costs grow uncontrollably
The problem isn’t prompting.
It’s architecture.
I audit your LLM or RAG system and show you exactly
*what’s breaking
*why it’s happening
*& how to fix it
This is not generic advice.
You get a clear, production focused architecture review with actionable fixes.
What I analyze
*Retrieval accuracy and ranking quality
*Latency and system bottlenecks
*Token usage and cost inefficiencies
*Prompt and context design issues
*Overall system architecture & scalability
What you get
*12–20 page architecture audit report
*Prioritized, actionable recommendations
*Clear next steps for optimization
Why me?
13 years of enterprise engineering experience at
*IBM
*Bank of America
*TIAA
Built AI systems processing 10,000+ documents per day on Azure OpenAI in production.
I don’t just design systems. I build ones that actually work.
Send me a brief about your system.
I’ll tell you straight if this audit will help, before you spend anything.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceWhat's included
| Service Tiers |
Starter
$750
|
Standard
$950
|
Advanced
$1,850
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 16 days |
Number of Revisions | 1 | 2 | 4 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
About Rams
AI Engineer | RAG & LLM Systems | Node.js, Azure, OpenAI, AWS
Morrisville, United States - 11:21 pm local time
Azure AI-102 certified. AWS Solutions Architect.
If your AI product needs to actually work in production, let's talk.
──────────────────────────────────────
𝗪𝗵𝘆 𝗺𝗼𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗮𝗳𝘁𝗲𝗿 𝗹𝗮𝘂𝗻𝗰𝗵
They work in demos. They break with real users, real data & real scale bcz they were built as API wrappers, not engineered systems. Latency spikes. Context windows overflow. Retrieval returns noise.
The fix isn't more prompting. It's architecture.
──────────────────────────────────────
𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝗱
• RAG systems. Document intelligence, knowledge bases, hybrid vector + keyword retrieval (Pinecone, FAISS, ChromaDB)
• LLM agents with memory, tool-calling & multi-step reasoning (LangChain, OpenAI Assistants API)
• AI automation pipelines. Replace manual workflows with intelligent, auditable systems
• Azure OpenAI & AWS Bedrock integrations inside enterprise grade backends
• AI powered SaaS MVPs from architecture to production deployment
• Document processing & OCR pipelines structured extraction from PDFs, contracts, forms
• Serverless LLM backends Node.js, AWS Lambda, Azure Functions
──────────────────────────────────────
𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗺𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁
Most AI freelancers come from ML research or frontend.
I come from 13 years of backend heavy enterprise engineering. The kind where performance, security, and reliability are non negotiable.
At TIAA, I architected an Azure OpenAI document processing system handling financial documents at scale reducing manual extraction effort by 60%+ and processing 10,000+ documents per day.
At IBM, I built chatbot infrastructure and Node.js microservices deployed on Kubernetes.
At Bank of America, I shipped client facing web applications under strict ADA and security compliance.
I know what enterprise systems actually require. Now I apply that same standard to AI products.
──────────────────────────────────────
𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
🏆 Azure AI Engineer Associate (AI-102)
🥇 Generative AI / LLM Specialization
🥈 AWS Certified Solutions Architect
──────────────────────────────────────
𝗟𝗲𝘁'𝘀 𝗯𝘂𝗶𝗹𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀.
Send me a message with what you're building. I'll tell you in 24 hours whether I can help & how.
Steps for completing your project
After purchasing the project, send requirements so Rams can start the project.
Delivery time starts when Rams receives requirements from you.
Rams works on your project following the steps below.
Revisions may occur after the delivery date.
System Overview
Understand your AI setup, goals, and key issues
Architecture Audit
Analyze LLM, RAG pipelines, and integrations