An agentic-AI finance tracker that ingests transactions and reasons over them with tool-using agents to surface spend insights.
The problem
Budgeting apps categorise transactions. They do not reason about them. Knowing you spent $412 on “Shopping” is data; knowing that it repeats every month and is drifting upward is an insight, and that gap is where the useful part lives.
How it works
- Transactions are ingested and handed to tool-using agents that can query, aggregate, and compare rather than simply label.
- Agents reason over spending history to surface patterns — recurrence, drift, and outliers — instead of producing another pie chart.
- The agent layer is the product: the interesting engineering is in giving a model the right tools and the right guardrails over personal financial data.
Why it exists
A working demonstration that agentic AI is most useful where the question is open-ended and the data is boring.
More work
Hiring for AI or ML?
I am open to AI/ML Engineering, Data Science, and Python roles, plus research collaborations and consulting. New York based, shipping worldwide.