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
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