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Patrick Gibbs

pro

• Mar 13

CRM automation system with a custom RAG (Retrieval-Augmented Generation) layer for a private client. The AI can answer questions using the client's actual business data, not generic responses.

Patrick Gibbs

pro

• 1d

Built end-to-end CRM workflow automation with a custom RAG-based AI knowledge base. Client can query their business data via natural language. Reduced research time by 60%.

AI Automation Claude Google Antigravity

Caleb Mccombs

• 1d

DM Co-Pilot: Production RAG AI Platform

I built this end-to-end RAG app to query complex datasets. Using Python and Streamlit, it delivers sub-second semantic search results from PDF uploads.

Technical Highlights:

RAG Pipeline: Uses LangChain and OpenAI for context-aware AI.

Data Visuals: Custom 'Web of Fates' graph built with PyVis.

Performance: Deployed on Render; handles high-concurrency.

Pro UI: Clean dashboard for seamless AI interaction.

AI Development OpenAI Streamlit AI Agent Engineer AI Engineer Python

Caleb Mccombs

• 1d

DM Co-Pilot: AI RAG Platform

I built this end-to-end RAG application to query complex datasets. Using Python and Streamlit, it delivers sub-second semantic search results from PDF uploads.

Technical Stack:

RAG Pipeline: Built with LangChain and OpenAI.

Data Visuals: Interactive graphs via PyVis.

Infrastructure: Deployed on Render for high-concurrency.

Streamlit AI Agent Engineer AI Agent Designer AI Engineer OpenAI Python