Building an AI Agent That Searches the Web and Makes Investment Decisions

In this video I build a local agentic AI pipeline that analyzes a bond portfolio and makes sell/hold decisions based on risk analysis and live web search data.

The agent runs four steps: load portfolio positions from JSON, classify each position as low/medium/high risk, search the web per position via Tavily API for historical performance and current outlook, then make a final sell/hold decision with reasoning — all powered by Gemma 4 31B running locally on Apple Silicon via mlx-vlm. No data leaves your machine.

All steps orchestrated with Prefect.

🔗 GitHub: https://github.com/katanaml/sparrow
🌐 Live: https://sparrow.katanaml.io
📧 Enterprise inquiries: abaranovskis@redsamuraiconsulting.com

Building Agentic AI Pipelines for Document Analysis

In this video, I show how to build a local agentic AI pipeline using Sparrow to extract and analyze data from financial documents. 

 The agent runs two steps: 

- Extract structured data from a bonds table image using Sparrow Parse pipeline and Ministral 3B 14B model 

- Analyze portfolio risk using Sparrow Instructor pipeline and Gemma 4 31B model — classifying each position as low, medium, or high risk

 

Both steps run as Prefect tasks inside a single flow, fully locally — no data leaves your machine.