You will get a migration that cuts your OpenAI bill using Qwen or DeepSeek
Project details
If your product runs on GPT-4 class calls, your LLM bill is probably your fastest growing cost, and a lot of those calls do not need a frontier model. Qwen and DeepSeek handle classification, extraction, summarization, and routine chat at a fraction of the price. The catch is doing it without quietly degrading quality.
I migrate the calls that can safely move, prove the savings with real numbers, and keep the premium model where it earns its cost.
What you get:
• A cost audit showing where your LLM money goes
• A plan for which calls move to Qwen or DeepSeek and which stay
• A quality eval on your real prompts, so we compare old vs new output
• Smart routing: cheap model by default, premium fallback on hard cases
• A before and after report with real cost and quality numbers
Most migrations fail by swapping models blind, with no eval and no fallback. I build those first, so savings never cost you quality you cannot see.
I work with OpenAI, Anthropic, Qwen, and DeepSeek, and I read and write Chinese, which helps with the Chinese model docs. Start with the audit tier and you get the numbers first.
I migrate the calls that can safely move, prove the savings with real numbers, and keep the premium model where it earns its cost.
What you get:
• A cost audit showing where your LLM money goes
• A plan for which calls move to Qwen or DeepSeek and which stay
• A quality eval on your real prompts, so we compare old vs new output
• Smart routing: cheap model by default, premium fallback on hard cases
• A before and after report with real cost and quality numbers
Most migrations fail by swapping models blind, with no eval and no fallback. I build those first, so savings never cost you quality you cannot see.
I work with OpenAI, Anthropic, Qwen, and DeepSeek, and I read and write Chinese, which helps with the Chinese model docs. Start with the audit tier and you get the numbers first.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging FaceAI Models
ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$400
|
Standard
$1,200
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
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
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FO
Fatih O.
Jun 30, 2026
MCP Server Integration & Voice Assistant MVP Development (1-2 Week Sprint)
Working with Yu has been an absolute pleasure and an incredible journey. We just wrapped up a high-intensity, two-week sprint for a demo project, and the dedication Yu showed was unmatched. Yu is like a marathon runner—extremely fast, highly efficient, and was practically online whenever I logged in, completely shattering any timezone barriers. What I admired most was the obsession with the smallest details, which truly elevated our project to a remarkable level. Even though we only communicated via text, we built a fantastic rhythm and a strong bond of trust. If you are looking for a brilliant developer who genuinely cares about the craft and delivers flawless work under a clock, look no further. I will absolutely be opening a new contract with Yu very soon. Highly, highly recommended!
About Yu Fong
AI Voice Agent Developer | Vapi, Retell, ElevenLabs, Twilio, MCP
Taipei, Taiwan - 8:43 am local time
Today I run 12 conversational AI agents live in production: they listen, classify intent, answer what's in scope, and hand off to a human the moment they're out of their depth — with memory, dedup, anti-loop guards, and confidence-gated escalation so they don't hallucinate or spam. I've shipped omnichannel support inboxes end to end (Telegram, WhatsApp, Crisp), earned 5★ on my last delivery, and built a production voice assistant in a high-stakes domain (multi-LLM voice pipeline + MCP tool-calling).
Where voice and chat agents actually break isn't the model — it's the seams: latency, flaky function-calling, no clean human handoff, no state recovery after a restart. That's my lane. Stack: Vapi/Retell, Twilio, ElevenLabs/Deepgram, LLM function-calling, FastAPI webhooks, MCP, Python.
What I won't waste your time on: "AI strategy" decks, ChatGPT wrappers you could build yourself, or a demo that was never built to survive real customers. I quote, scope, ship, and hand off documented code with a runbook.
Taipei (GMT+8). Text-first, fluent written English, same-day replies.
Steps for completing your project
After purchasing the project, send requirements so Yu Fong can start the project.
Delivery time starts when Yu Fong receives requirements from you.
Yu Fong works on your project following the steps below.
Revisions may occur after the delivery date.
Audit your LLM usage and costs
I map where your spend goes and which calls dominate the bill.
Pick what can move and build a quality eval
I select the calls that can safely migrate and build an eval on your real prompts.