CASE FILE / AI MVP / FINTECH RESEARCH
AI stock research MVP
How Sprint966 turned hours of fragmented stock analysis into a focused AI-powered research report workflow.
- FinTech
- AI Research
- Stock Analysis
- DCF
- PDF Reports
- Streamlit
- MVP
The core workflow
One loop the whole MVP is built around
- Analyze the stock
- Score the company
- Estimate value
- Generate report
- Client
- Investment research / FinTech
- Industry
- FinTech / Equity research
- Engagement
- AI-powered stock analysis MVP
- Market focus
- U.S. small and mid-cap stocks
- Timeline
- ~68 hours / 3 weeks
- Sprint966 role
- MVP scoping, backend & API development, database integration, Streamlit integration, PDF reporting, performance optimization
The challenge
The challenge was not building a dashboard. It was deciding how little to build.
Stock research is slow, fragmented, and easy to drown in. The MVP had to compress financial data, scoring, valuation, and reporting into one workflow without becoming a Bloomberg-sized platform before demand was proven.
Clarity before code
The decision: prove the research workflow first
Can an investor look at a small or mid-cap stock and quickly understand whether it is attractive — backed by structured financial analysis, not a hunch?
- Analyze the stock
- Score the company
- Estimate value
- Generate report
Build vs. Cut
Focus, not limitation
Stayed in the MVP
- Stock ticker workflow
- Six-category scoring engine
- DCF valuation module
- PDF report generation
- Streamlit analysis dashboard
- Backend APIs
- Database integration
- Secure authentication
- Report history foundation
- Performance optimization
Moved to roadmap
- Live market feeds
- Portfolio analysis
- Watchlists
- Saved reports
- Peer and sector comparison
- Custom DCF assumptions
- Subscription payments
- Email report delivery
- Institutional reporting templates
- Richer AI commentary
What we built
Four product modules
Scoring engine
Six categories: valuation, profitability, growth, financial health, efficiency, and management quality — rolled into one explainable score.
DCF valuation module
Forecasted cash flows, discount rate, terminal value, and comparison against market price.
PDF research reports
Company overview, key metrics, score, DCF, and investment case in one shareable document.
Streamlit + backend integration
Streamlit dashboard embedded into the branded website with backend APIs, database integration, authentication, and custom JavaScript wiring.
What the output looks like
A ticker becomes a structured investment case
- Example stock
- Alpha Metallurgical Resources (NYSE: AMR)
- Overall score
- 80 / 100 — “Strong”
- DCF value
- $315.04 per share
- Market price
- $197.04
- Model read
- Roughly 37% undervalued
- P/E
- 6.05
- ROIC
- 28%
The platform is built for research and education, not personalized financial advice. Its output is a starting point for analysis, not a recommendation.
Built to scale, not just to demo
A backend-first foundation
- Backend APIs
- Secure authentication
- User database
- Report generation
- Streamlit integration
- Custom JavaScript
- Planned caching
- Performance optimization
- Report history
- Website embedding
Results
What the MVP delivered
- A six-category scoring engine producing an explainable score out of 100.
- A DCF module estimating intrinsic value per share against market price.
- Automated, downloadable PDF research reports.
- A Streamlit analysis dashboard embedded cleanly into the website.
- A secure, backend-first foundation built for future scale.
- A working sample report proving the end-to-end workflow.
- Full MVP scoped and delivered in three weeks.
Impact
A research process that used to mean hours of manual ratio-hunting now produces a structured investment case in one pass.
For the client, that turns a complex idea into something they can put in users’ hands — to test demand, generate research, and grow toward a subscription platform, portfolio scoring, and deeper AI commentary.
Where it goes next
The roadmap ahead
- Live market data
- Watchlists
- Saved reports
- Peer comparison
- Sector comparison
- Custom DCF assumptions
- Subscription payments
- Email report delivery
- Institutional templates
- Richer AI commentary
Have an AI product idea but aren’t sure what to build first?
Let’s scope the first version before the platform gets too big.