You will get a local AI chat system for your team — runs on your hardware, no cloud
Project details
Most AI tools send your data to a cloud provider. This project sets up a private AI chat system that runs entirely on your own machine — your documents, your conversations, nothing leaves your hardware.
What you get: Ollama installed and configured on your machine, a local model selected and pulled (Llama, Mistral, Gemma, or similar based on your hardware), a simple web chat interface your team can use without a terminal, and a README with run and restart instructions.
This isn't a hosted service — it runs on your hardware, uses your compute, and stays on your network. I've run the same stack on my own 4×H100 GPU cluster, so I know where the setup actually breaks: model memory fit, quantization choice, context window limits, and the differences between a dev machine and a team server.
Best fit: teams handling sensitive documents, businesses in regulated industries (legal, healthcare, insurance, finance), or anyone who needs AI that genuinely cannot phone home.
Delivered with a setup document so you can restart, swap models, and adjust settings yourself.
Not included: cloud AI integration, fine-tuning or training, deployment to external servers.
What you get: Ollama installed and configured on your machine, a local model selected and pulled (Llama, Mistral, Gemma, or similar based on your hardware), a simple web chat interface your team can use without a terminal, and a README with run and restart instructions.
This isn't a hosted service — it runs on your hardware, uses your compute, and stays on your network. I've run the same stack on my own 4×H100 GPU cluster, so I know where the setup actually breaks: model memory fit, quantization choice, context window limits, and the differences between a dev machine and a team server.
Best fit: teams handling sensitive documents, businesses in regulated industries (legal, healthcare, insurance, finance), or anyone who needs AI that genuinely cannot phone home.
Delivered with a setup document so you can restart, swap models, and adjust settings yourself.
Not included: cloud AI integration, fine-tuning or training, deployment to external servers.
AI Algorithms
Large Language ModelAI Applications
AI ChatbotAI Models
LLaMAWhat's included
| Service Tiers |
Starter
$299
|
Standard
$599
|
Advanced
$799
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 2 |
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 Rico
26yr PPA-MGA | AI Evaluator - Claims, UW, Persistency, Growth, Rate
Phoenix, United States - 2:42 am local time
If you're building AI to work with insurance documents — annotation, classification, extraction, or validation — domain accuracy is what separates a useful tool from a liability. I've built a live automated rate engine that parses six carrier JSON structures against actual SERFF filings. I know when a model is wrong, why it's wrong, and exactly what the right answer should be.
What I offer: field-level annotation grounded in real operations, not textbook definitions. Policy coverage terms, claims field semantics, rate factor logic — explained in the language your training pipeline needs.
I also build automation infrastructure: Python pipelines, browser tools with human approval gates, and private AI deployments that run on your own hardware with no data leaving your network.
Send me a sample document and tell me what your model needs to get right.
This rate is for people who need it done right. Fixed-price, satisfaction guaranteed — if you're not satisfied, nothing is owed.
Steps for completing your project
After purchasing the project, send requirements so Rico can start the project.
Delivery time starts when Rico receives requirements from you.
Rico works on your project following the steps below.
Revisions may occur after the delivery date.
Install & configure local AI system
Ollama, selected model, and web chat interface installed on your machine and tested.