
FilingIQ
Financial filing intelligenceExplore sourced financial figures from U.S. public company filings, compare annual trends, and export reproducible results.
SELECTED ENGINEERING WORK / 2026
Three working applications built around a practical question: can a system turn complex public data into something a person can verify, understand, and act on?
Each project has a working interface, dataset provenance, a measurable workflow, and documented limitations. Open one and try it.

Explore sourced financial figures from U.S. public company filings, compare annual trends, and export reproducible results.

Train an interpretable ridge model on U.S. electricity observations, test it chronologically and compare against a seasonal baseline.

Browse public GitHub pull requests, inspect changes, and review deterministic warnings with clear explanations.
02 / HOW I BUILD
A good interface helps you discover the answer. A good system lets you see where it came from.
Public data is cited; synthetic fixtures are identified; and out-of-date snapshots are labelled.
ML results are measured against simple alternatives instead of relying on model buzzwords.
APIs can rate-limit or go down. The app communicates what happened and offers a useful fallback.
Each implementation includes automated tests and a documented local setup path.