You will get a secure privacy-first local AI agent using LangGraph
Rising Talent
Rising Talent
Project details
Stop sending your sensitive company data to public APIs! >
Are you a Healthcare, Legal, or FinTech startup that needs high-performance AI but is blocked by strict data privacy laws (HIPAA/GDPR)? Do you want to avoid massive monthly API bills while maintaining 100% data sovereignty?
I am an AI Engineer specializing in 100% Local, Privacy-First Agentic AI Workflows. I build advanced AI systems that run entirely on your own hardware or private cloud, ensuring your data never leaves your servers.
Hardware-Optimized Local LLMs: I deploy heavily quantized models (Llama-3 8B, Qwen) that run smoothly on constrained consumer GPUs using memory-efficient techniques like the VRAM Singleton Pattern.
Stateful AI Agents (LangGraph): Moving beyond simple chatbots. I build deterministic directed acyclic graphs (DAGs) with strict routing and reasoning nodes.
Human-in-the-Loop (HITL): Implementing robust pause-and-resume approval gates. Your experts stay in control while the AI automates the heavy lifting.
Sub-second RAG Retrieval: Integrating Redis semantic caching and ChromaDB to drop API latency from seconds to <100ms.
Let's build an AI system that is secure, deterministic, and belong to you
Are you a Healthcare, Legal, or FinTech startup that needs high-performance AI but is blocked by strict data privacy laws (HIPAA/GDPR)? Do you want to avoid massive monthly API bills while maintaining 100% data sovereignty?
I am an AI Engineer specializing in 100% Local, Privacy-First Agentic AI Workflows. I build advanced AI systems that run entirely on your own hardware or private cloud, ensuring your data never leaves your servers.
Hardware-Optimized Local LLMs: I deploy heavily quantized models (Llama-3 8B, Qwen) that run smoothly on constrained consumer GPUs using memory-efficient techniques like the VRAM Singleton Pattern.
Stateful AI Agents (LangGraph): Moving beyond simple chatbots. I build deterministic directed acyclic graphs (DAGs) with strict routing and reasoning nodes.
Human-in-the-Loop (HITL): Implementing robust pause-and-resume approval gates. Your experts stay in control while the AI automates the heavy lifting.
Sub-second RAG Retrieval: Integrating Redis semantic caching and ChromaDB to drop API latency from seconds to <100ms.
Let's build an AI system that is secure, deterministic, and belong to you
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Text-to-Image, Conversational AI, Image Analysis, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Gradio, Hugging Face, PyTorch, StreamlitAI Models
LLaMAWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 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 |
Optional add-ons
You can add these on the next page.
Cloud GPU Deployment Setup (RunPod/AWS)
(+ 2 Days)
+$300
Slack / Telegram Integration
(+ 3 Days)
+$250Frequently asked questions
About Cao Tri
Data Scientist | Computer Vision & NLP | Transformers & MetricLearning
Spring Mountain, Australia - 6:38 pm local time
My expertise spans the full spectrum of image classification technologies:
Resource-Efficient ML: I build lightweight, CPU-friendly pipelines using classical feature engineering (HOG, PCA) and Random Forests, ideal for scenarios where training speed and low resource usage are critical.
Deep Metric Learning: For fine-grained recognition tasks where classes look very similar, I engineer DCNNs (such as ResNet) using Hard Triplet Loss to optimize embedding spaces and improve cluster separation.
State-of-the-Art Transformers: When maximum accuracy is paramount, I deploy advanced architectures like Swin Transformers and ViT to capture complex global features and effectively handle noisy backgrounds.
Whether you need a fast model for edge devices or SOTA accuracy for complex datasets, I have the experience to build, optimize, and validate the best model for your goals.
Steps for completing your project
After purchasing the project, send requirements so Cao Tri can start the project.
Delivery time starts when Cao Tri receives requirements from you.
Cao Tri works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture & Hardware Feasibility
I will analyze your use case, select the optimal local LLM (e.g., Llama-3 8B), and design the LangGraph workflow tailored to your hardware limits.
RAG Pipeline & Core Agent Build
I will build the local vector database, configure semantic caching (Redis), and program the LangGraph nodes for deterministic data retrieval.