You will get Enterprise AI Optimization: Token Cost Reduction & Latency Tuning
Project details
You've shipped AI features to production. Your users love them. Your CFO does not.
The gap between "prototype AI" and "production AI" is where most teams fail. A chatbot that works in a demo can bleed $50K/month in API costs when scaled. A feature with acceptable latency at 10 concurrent users collapses under real traffic. Architectures built for speed of development, not speed of execution, become technical liabilities.
This project is for teams running real AI in production who are experiencing exploding LLM and cloud compute costs, unacceptable latency that harms user experience, or architectures that never scaled properly.
I provide senior-level MLOps engineering to optimize your AI systems for production: lower unit economics, faster response times, and reliable scaling. This is not prompt engineering tutorials, ChatGPT wrappers, or proof-of-concept work. This is production optimization for teams with real users and real costs.
The gap between "prototype AI" and "production AI" is where most teams fail. A chatbot that works in a demo can bleed $50K/month in API costs when scaled. A feature with acceptable latency at 10 concurrent users collapses under real traffic. Architectures built for speed of development, not speed of execution, become technical liabilities.
This project is for teams running real AI in production who are experiencing exploding LLM and cloud compute costs, unacceptable latency that harms user experience, or architectures that never scaled properly.
I provide senior-level MLOps engineering to optimize your AI systems for production: lower unit economics, faster response times, and reliable scaling. This is not prompt engineering tutorials, ChatGPT wrappers, or proof-of-concept work. This is production optimization for teams with real users and real costs.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, Conversational AIAI Tools
Azure OpenAIAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$2,000
|
Standard
$5,000
|
Advanced
$10,000
|
|---|---|---|---|
| Delivery Time | 5 days | 14 days | 30 days |
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
About Matt
Senior Full-Stack Engineer | Angular & Node.js Expert | 10+ Years Ente
Las Vegas, United States - 9:31 pm local time
I've spent the last decade architecting full-stack systems for Fortune 500 companies. The pattern is always the same: clients come with complex problems (real-time dashboards, offline-tolerant systems, multi-tenant architecture). I solve them. Usually ahead of schedule.
What I deliver:
- Production-grade Angular/Node.js systems handling thousands of concurrent users
- Resilient cloud infrastructure (AWS/Azure) with CI/CD pipelines that don't break
- Data visualization dashboards that make complex data intuitive
- Code that's actually maintainable 6 months from now
Recent wins:
Charter Communications (real-time analytics dashboards) | United Airlines (offline-tolerant kiosks) | Disney/FOX (enterprise-scale employee/payment tracking)
Right now: If you're looking for someone who'll own the technical architecture—not just code—let's talk.
Steps for completing your project
After purchasing the project, send requirements so Matt can start the project.
Delivery time starts when Matt receives requirements from you.
Matt works on your project following the steps below.
Revisions may occur after the delivery date.
Technical Audit & Benchmarking
Review codebase, cloud infrastructure, and LLM integrations. Profile token usage, latency, and cost. Identify bottlenecks.
Optimization Strategy Presentation
Present findings and prioritized recommendations. Discuss cost/quality tradeoffs. Agree on scope and timeline.