You will get an enterprise multimodal rag agent system
Project details
Leveraging a Ph.D. in AI and over 8 years of applied research and commercial engineering experience, this service focuses on designing customized RAG agent systems capable of processing complex multimodal data, while strictly optimizing for computational efficiency and minimal viable product cost.
Project Delivery
Feasibility Assessment & Model Selection: Objective technical evaluation of business requirements. Identification of the optimal foundation models and application frameworks to minimize API and compute costs without sacrificing core performance.
Architecture & Workflow Optimization: Designing decoupled, robust agent workflows. Implementing precise serial and parallel processing strategies at the pipeline level to optimize system latency and throughput.
Cost-Optimized MVP Validation: Rapid prototyping of the core agent loop to validate business logic and technical feasibility at the lowest possible expenditure.
Commercial Deployment: Scaling the validated MVP into a production-ready application with strict output validation, error handling, and structured data extraction.
Project Delivery
Feasibility Assessment & Model Selection: Objective technical evaluation of business requirements. Identification of the optimal foundation models and application frameworks to minimize API and compute costs without sacrificing core performance.
Architecture & Workflow Optimization: Designing decoupled, robust agent workflows. Implementing precise serial and parallel processing strategies at the pipeline level to optimize system latency and throughput.
Cost-Optimized MVP Validation: Rapid prototyping of the core agent loop to validate business logic and technical feasibility at the lowest possible expenditure.
Commercial Deployment: Scaling the validated MVP into a production-ready application with strict output validation, error handling, and structured data extraction.
AI Algorithms
Autoencoder, Convolutional Neural Network, Large Language Model, Multilayer Perceptron, Multimodal Large Language Model, Recurrent Neural Network, StyleGAN, Transformer Model, Variational Autoencoder, YOLOAI Applications
AI Chatbot, AI Text-to-Image, AI Text-to-Speech, AI-Generated Code, Automatic Speech Recognition, Image Processing, Image Recognition, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, PyTorch, TensorFlowAI Models
BERT, ChatGPT, GPT-4, LLaMA, OpenAI Codex, Stable DiffusionWhat's included
| Service Tiers |
Starter
$200
|
Standard
$2,500
|
Advanced
$9,999
|
|---|---|---|---|
| Delivery Time | 9 days | 16 days | 70 days |
Number of Revisions | 3 | 3 | 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 |
About Su
Ph.D. in AI | AI Architect & Corporate Trainer | LLM, RAG & Vision
Qingdao, China - 8:13 pm local time
As a Ph.D. in AI Research with 5 years of frontline industrial R&D experience (formerly at Fujitsu & KDDI), I audit failing AI infrastructures, decouple perception from cognition, rescue broken Agentic pipelines. I then translate these hard-won fixes into project-based training, so your team learns to build reliable systems, not just prototypes.
[Credibility & Authority]
• Global Academic Authority: Ph.D. in AI from Kyushu University. First author of an IEEE TEVC Outstanding Paper (3,500+ citations, featured by BBC News & Hacker News) on deep neural network vulnerabilities.
• Enterprise R&D Track Record: Former Researcher at Fujitsu Ltd. and KDDI Research. Specializing in translating complex academic theories into highly robust, commercial-grade visual recognition. Former Tech lead at famous global startups., operating closely with former members from AMAZON, GOOGLE, NASA etc to build a proprietary multimodal foundation model from scratch.
• Cross-Border Tech Liaison: I seamlessly conduct technical due diligence and align complex architectural specifications across global engineering teams and management boards.
• Deep Experience in AI Training: I design and implement comprehensive, project-based AI training programs tailored for both technical development teams and non-technical enterprise staff. Drawing from my experience as an internal corporate AI instructor, I provide end-to-end educational solutions ranging from curriculum planning and proprietary material development to live lecturing and one-on-one hands-on coaching.
[Core Consulting Solutions]
1. AI-OCR System Rescue & Pipeline Re-engineering
I specialize in rebuilding failing document processing pipelines due to serious hallucinations. I dismantle complex, untraceable monolithic prompts and re-engineer them into explicit, multi-agent workflows. By decoupling tasks based on complexity and volume, I design and deploy targeted sub-agents, specialized tool nodes, and confidence-based routing mechanisms. This optimized serial and parallel orchestration—coupled with rigorous task-specific evaluation metrics, completely eradicates the chronic hallucinations, token waste, and low usability inherent in raw multimodal OCR deployments.
2. Agentic RAG Architecture & AI Cost Optimization
I perform deep Technical Due Diligence on current AI infrastructure. I have previously guided enterprise IT teams to bypass overpriced, opaque third-party wrappers, and reduce monthly AI operational costs by 75% while significantly mitigating data toxicity.
3. Vision Foundation Models & Specialized PoC Commercialization
Leveraging my background in engineering spatio-temporal large vision models from scratch, I design robust architectures for specialized domains, including large-scale satellite imagery analysis and medical deep learning systems for disease recognition. I guide the strategic direction from edge-case data preprocessing to specialized fine-tuning and cloud-native commercial deployment.
4. Enterprise AI Curriculum Design & Technical Mentorship
Drawing on my deep engineering background and extensive experience as an internal corporate educator across top-tier enterprises(3 curriculums, 64 hours+ in total), I design and deliver highly customized, project-based AI masterclasses. I bypass generic tutorials to address real-world business pain points, guiding engineering teams to build GraphRAG-enabled chatbot agents from scratch or optimize workflows for their specific operational needs. From cross-departmental technical alignment to executive-level strategic workshops, I handle end-to-end curriculum planning, material development, and hands-on technical mentoring.
5. Scalable Human-Object Interaction (HOI) Architecture
Traditional HOI models fail to capture true interaction semantics and struggle to generalize to unseen classes. I spearhead zero-to-one R&D and commercial deployment of advanced anomaly detection architectures for large-scale retail and manufacturing environments. By integrating multimodal architectures and contrastive learning, I resolve foundational bottlenecks in generalization, enabling highly robust anomaly detection for complex physical operations and bridging the gap between academic theory and pragmatic industrial applications.
KEYWORDS:
- Artificial Intelligence
- Agentic RAG Architecture
- Technical Due Diligence
- AI Cost Optimization
- Multi-Agent Orchestration
- Multimodal Vision Foundation Models
- Vision-Language Models
- Enterprise AI Mentorship
- AI Curriculum Development
- Corporate AI Training
Struggling pipeline or unsure about training? Describe your bottleneck, then I'll give a candid, no-obligation diagnostic.
Steps for completing your project
After purchasing the project, send requirements so Su can start the project.
Delivery time starts when Su receives requirements from you.
Su works on your project following the steps below.
Revisions may occur after the delivery date.
Prior discussion
Discussing the technical issues and possible solutions
Working progress
Constantly discussing with the client, getting feedbacks and revising the work.