Smarter Investment Research
We helped a financial company simplify stock research by creating a digital platform that generates faster, more consistent investment reports.
- FinTech
- AI Research
- Stock Analysis
- DCF
- PDF Reports
- Streamlit
Our Process
A Smarter Way to Analyze Investments
From Data to Insights in Four Simple Steps
- Review Data
- Evaluate the Company
- Estimate Value
- Generate Report
- Client
- Investment research / FinTech
- Industry
- FinTech / Equity research
- Engagement
- AI-powered stock analysis platform
- Market focus
- U.S. small and mid-cap stocks
- Timeline
- 3 Weeks
- Sprint966 role
- Product scoping, backend and API development, database integration, Streamlit integration, and reporting.
The Challenge
Turning Slow Research into Faster Decisions
Investment research was slow, scattered, and time-consuming. Analysts needed a faster, simpler way to evaluate companies and make confident decisions.
Our Solution
We Built a Smarter Research Experience
A streamlined digital platform that brings data, analysis, and reporting together in one simple workflow—helping users save time and make better investment decisions.
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 platform 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 scope 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.