You will get an AI workflow automation or custom RAG system for your business
Rising Talent
Project details
I turn repetitive, document-heavy work into reliable AI automation. Whether you need a custom RAG system that answers questions over your own documents (with citations, not hallucinations) or an LLM workflow that automates a multi-step process, I build it to run in production: clean chunking and retrieval, evaluation so accuracy is measured, logging, and guardrails. Senior, founder-led delivery — you own every line of code. I handle the unglamorous parts (data prep, integration, reconciliation) so the AI on top actually holds up.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 3 days | 10 days | 15 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 | - | - | - |
About Nguyen
Integration & Security Architect | API, HL7 FHIR, HIPAA, PCI DSS, Node
Hanoi, Vietnam - 8:03 am local time
I build production-grade software and applied AI for regulated industries — healthcare (HIPAA), fintech (PCI DSS) and manufacturing — where clean data, security and the audit trail actually matter. Senior, founder-led. Fixed price. You own every line of code.
Most projects don't fail on the clever part; they fail on the unglamorous 99% — data pipelines, integration, reconciliation, compliance. I build that foundation first, so the AI on top actually works and keeps working.
What I do best:
• HIPAA compliance audits & architecture reviews — gap analysis, remediation roadmap, audit-ready documentation
• Custom platforms & modernization — multi-tenant SaaS, API/gateway layers, legacy EHR/ERP/CRM modernization
• Applied & agentic AI — prediction, document intelligence and decision support on regulated data, human-in-the-loop, accuracy measured
• Security & compliance by design — HIPAA-aligned (BAA-backed), architected to PCI DSS 4.0, least-privilege, audit logging
• Real-time data & integration — IoT/MES telemetry, HL7/FHIR, payment gateways, reconciliation, monitoring
• Retrieval & LLM systems — RAG pipelines, chunking and embeddings (bge-m3), vector search (FAISS, Chroma, pgvector), LangChain, MCP servers, retrieval evaluation
• Healthcare interoperability — HL7v2 messaging, FHIR R4 (Patient, Appointment, Encounter, Observation), SMART on FHIR / OAuth2 launch flows
Selected results:
• 5.0/5.0 on Clutch for a HIPAA-aligned AI platform (investor-ready, pharma)
• Multi-tenant EHR live across 4 US clinics, ~300 patients/day
• Telehealth platform scaled: +60% capacity, −40% cost, −20% overhead
• One API across 3+ payment processors without pulling the app into PCI scope
• Open-source AI: Kite (agent framework) and Nebula (on-device GraphRAG)
• Author, "Healthcare AI That Works" — Amazon #1 Bestseller in Medical Informatics
Whether you need a HIPAA audit, an investor-ready MVP or a critical system modernized, I start with a senior architecture review, then give you a fixed price and a plan. Message me and I'll map it with you.
Steps for completing your project
After purchasing the project, send requirements so Nguyen can start the project.
Delivery time starts when Nguyen receives requirements from you.
Nguyen works on your project following the steps below.
Revisions may occur after the delivery date.
Kickoff & scoping
Confirm the process or documents, data sources and volume, where it runs, your LLM preference, and what success looks like.
Build the RAG / automation
Implement retrieval or the multi-step workflow with clean chunking, integrations, prompts, guardrails, and logging.