You will get AI-Powered Search & Knowledge Base Solutions | Custom RAG Systems
Project details
Our enterprise RAG (Retrieval Augmented Generation) system transforms how organizations interact with their data, enabling natural language search and delivering contextually relevant insights.
AI Development Type
Deep Learning, Model Tuning, Recommendation SystemAI Tools
Azure Machine Learning, Keras, MLflow, NVIDIA AI Platform, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$999
|
Standard
$1,999
|
Advanced
$3,999
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 4 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | ||
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$299
Custom Integration Support
(+ 3 Days)
+$499About Shawn
Enterprise AI & RAG Systems Architect
Council Bluffs, United States - 3:25 am local time
• Underwood AI: underwood-ai.com
• Personal Portfolio: shawnunderwood.com
Enterprise AI architect with 8+ years of experience specializing in RAG (Retrieval Augmented Generation) systems and large-scale ML implementations. I help companies transform their data into searchable, actionable knowledge bases through custom AI solutions.
Live Projects:
• Huberman Archive (huberman-archive-production.up.railway.app)
- RAG-powered search system for podcast content
- Built with LLama, Qdrant, FastAPI, custom RAG pipeline
• VoicemailMachine (voicemailmachine.com)
- Enterprise voicemail processing platform
- Azure AI Speech, Kafka, Docker/K8s
• Patent Search API (patent-api-production.up.railway.app/docs)
- ML-enhanced patent validation system
- FastAPI, Redis, ML pipeline
Focused on delivering production-ready systems that provide immediate business value while maintaining enterprise-grade reliability and security. Strong emphasis on system architecture that scales with your data and user needs.
Steps for completing your project
After purchasing the project, send requirements so Shawn can start the project.
Delivery time starts when Shawn receives requirements from you.
Shawn works on your project following the steps below.
Revisions may occur after the delivery date.
Client purchases the project and sends requirements.
Obtain detailed requirements from the client, including access to data sources, specific search functionality needs, security/compliance requirements, and the implementation timeline.
Design and Implement RAG Pipeline
Develop the custom RAG (Retrieval Augmented Generation) pipeline architecture, integrating the scalable vector database and automating the content processing workflows.This will create the core foundation for the natural language search capabilities.