QNotes

Our landing page

Inspiration

Financial analysis training today is outdated: students and junior analysts are still reading hundreds of pages of 10-Qs and 10-Ks with little feedback. Meanwhile, professionals spend countless hours manually summarizing filings.

We were inspired by how medical education uses SOAP notes to teach clinical reasoning: structured, graded, and iterative.

Our idea was to bring that same revolution to finance — turning the overwhelming world of SEC filings into structured, learnable notes using AI.

What it does

QNotes transforms SEC filings, earnings transcripts, and financial statements into structured “F-Notes” using the SMAP framework:

S – Subjective: Management tone and qualitative insights M – Metrics: Key financial ratios and numbers A – Assessment: AI interpretation of performance and risks P – Plan: Recommended next steps or areas to monitor

The result is a one-page financial summary — clear, teachable, and interactive.

Users (students, analysts, advisors) can:

Generate AI-powered SMAP notes
Edit their own version
Receive AI feedback and grading on completeness, clarity, and insight depth
Learn through inline hints, flashcards, and quizzes
Compare their analysis to the AI gold standard SMAP

In essence: AI-powered note-taking, grading, and learning for financial filings.

What we learned

Finance professionals crave structured summarization, but students crave interactive feedback — combining both creates powerful engagement. The SMAP framework makes complex filings digestible and measurable. Contextual AI grading (not just right/wrong) helps users improve critical thinking faster. Building with structured frameworks (like SOAP → SMAP) is the key to scaling AI learning tools in any professional field.

How we built it

We used Figma to design our app and implemented our design using React Native. The entirety of the backend was completed using Python and the Snowflake API. We used Gemini and LangChain to implement our AI analysis and educational features. The voice agent was implemented with 11ElevenLabs. We used the SEC's Edgar API to collect our financial data and Supabase for DB management.

Challenges

Earnings statements can be over 50+ pages; naturally, this was a lot of data that was being collected using the Edgar API. The biggest challenge was using all this data with the Gemini API to deliver the relevant AI analyses.

What's next for QNotes

Phase 1: Expand coverage to include press releases, investor presentations, and ESG reports. Phase 2: Build an AI grader dashboard for professors and firms to assign and review SMAP notes. Phase 3: Launch a QNotes Learning Hub with skill tracking (e.g., “Risk Analysis” score improving over time). Phase 4: Integrate live market data and valuation models — connecting filings to real-world stock performance.

Long-term: Position QNotes as the EHR for finance — a universal, AI-driven documentation system for analysts, advisors, and students alike.