Version: 1.0

Date: May 28th 2025

Product Manager & Developer: Tharun Poduru

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1. Introduction

 

1.1. Product Name: FilingIntel

 

1.2. Goals:

• To provide users with rapid, easily digestible, and accurate financial insights from public company SEC filings.

• To leverage AI to offer a preliminary analysis of a company's financial health, saving users time and effort.

• To become a go-to tool for quick financial assessments of publicly traded companies.

 

1.3. Vision:

• FilingIntel aims to democratize access to financial data analysis, empowering individual investors, students, and professionals to make more informed decisions without needing to be SEC filing experts or spend hours on manual data extraction. We envision a platform where complex financial data becomes intuitive and actionable.

 

2. Target Audience

 

2.1. Primary Users:

• Retail Investors: Seeking to understand the financial health of companies before investing.

• Financial Analysts (especially Junior/Mid-Level): Requiring quick access to standardized financial metrics and ratios for initial assessments or report preparation.

• Business & Finance Students: Learning about financial statement analysis and needing real-world data examples.

 

2.2. Secondary Users:

• Journalists/Reporters: Needing quick financial facts for articles.

• Small Business Owners/Consultants: Performing competitor analysis on publicly traded companies.

 

3. User Stories & Use Cases

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• US1: As a retail investor, I want to quickly search for a company by its ticker symbol and see its key financial trends (like revenue, net income, OCF) over the past two years, so I can gauge its recent performance.

• US2: As a financial analyst, I want to view calculated ratios like Debt-to-Equity, Free Cash Flow, and ROE for a company without manually sifting through 10-K/10-Q filings, so I can speed up my initial due diligence.

• US3: As a finance student, I want to see how metrics like Operating Cash Flow are presented for different companies and how Q4 data can be derived from annual reports, so I can better understand financial reporting.

• US4: As any user, I want an AI-generated summary of a company's financial strengths and weaknesses upon loading its data, so I can get a quick, high-level understanding before diving into specific numbers.

• US5: As a user, I want to visualize financial data like revenue and net income in clear charts, so I can easily identify trends and patterns.

• US6: As a user, I want the application to handle inconsistencies in SEC reporting (e.g., different XBRL tags for the same metric) robustly, so I can trust the data presented.

• US7: As a user, I want the application to clearly indicate if a metric is "N/A" and ideally provide context if it's due to data unavailability vs. a calculation issue, so I am not misled.

 

4. Key Features

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F1: Company Search & Dashboard Access

• F1.1: Ability to search for U.S. publicly traded companies by ticker symbol or company name.

• F1.2: Selection of a company loads its dedicated financial dashboard.

 

F2: Financial Dashboard

• F2.1: Overview & AI Insights (Initial View)

• F2.1.1: Display basic company identifiers (Name, Ticker, CIK).

• F2.1.2: Strategic Loading Screen: Upon company selection, immediately display essential company data points (e.g., latest reported revenue, net income, CIK). This data will serve as initial context for the user while, in parallel, an AI (Gemini API) call is made to generate a financial health summary. This provides instant value and manages perceived latency for the AI analysis.

• F2.1.3: AI Analysis Section: Prominently display the AI-generated summary and insights once available. Clearly attribute this to "AI Analysis powered by Gemini."

• F2.2: Financials Tab

• F2.2.1: Quarterly display (up to the latest 8 unique quarters) of core financial metrics:

• Revenue (e.g., from RevenueFromContractWithCustomerExcludingAssessedTax, Revenues)

• Net Income (e.g., from NetIncomeLoss)

• Total Assets (e.g., from Assets)

• Operating Cash Flow (OCF) (e.g., from NetCashProvidedByUsedInOperatingActivities, CashFlowFromOperatingActivities)

• Total Liabilities (calculated: Assets - Equity, or from LiabilitiesAndStockholdersEquity - Equity, or Liabilities)

• Shareholder Equity (e.g., from StockholdersEquity)

• F2.2.2: Interactive line or bar charts for each metric, visualizing trends over the displayed quarters.

• F2.2.3: Clear labeling of periods (e.g., "Q1 2023," "Q4 2022").

• F2.2.4: Indication of data source (e.g., "10-Q" or "10-K derived Q4").

• F2.3: Key Metrics Tab

• F2.3.1: Display of calculated financial ratios and key performance indicators, primarily for the latest reported quarter or Trailing Twelve Months (TTM) where appropriate:

• Earnings Per Share (EPS - TTM, from Net Income & Common Shares Outstanding)

• Free Cash Flow (FCF - TTM, using OCF as a proxy, less CapEx if available in future iterations)

• Current Ratio (Current Assets / Current Liabilities)

• Quick Ratio ((Current Assets - Inventory) / Current Liabilities)

• Debt to Equity Ratio (Total Debt / Shareholder Equity)

• Return on Equity (ROE - TTM, Net Income / Shareholder Equity)

• Return on Assets (ROA - TTM, Net Income / Total Assets)

• Working Capital Ratio (same as Current Ratio)

• F2.3.2: Display percentage change from the prior comparable period for key metrics to show trends.

• F2.3.3: Graceful handling of "N/A" values when data is insufficient for calculation, with tooltips or brief explanations.

 

F3: Data Sourcing & Processing Engine

• F3.1: SEC Data Acquisition:

• Utilize the SEC EDGAR API to fetch company facts (XBRL data) and company filing histories.

• Implement a backend proxy for SEC API calls to manage API keys, User-Agent headers, and potentially introduce a caching layer.

• F3.2: Focused Filing Scope: Prioritize analysis of a defined set of recent filings (e.g., the latest 11 primary filings like 10-K, 10-Q) to ensure data relevance and processing speed.

• F3.3: Intelligent XBRL Tag Resolution:

• Maintain a mapping of desired financial concepts (e.g., "Revenue") to a list of potential XBRL tags reported by companies.

• Implement logic (findBestKey) to select the most appropriate and available tag for a given company and metric.

• For critical multi-tag metrics like Operating Cash Flow, collect data from all valid identified tags and then employ logic to select the most representative fact for each quarter (considering filing dates and end dates).

• F3.4: Accurate Quarterly Data Aggregation:

• Extract data from 10-Q filings directly.

• Calculate Q4 data by subtracting the sum of Q1, Q2, and Q3 values (from 10-Qs) from the corresponding 10-K annual value. Implement validation checks for derived Q4 figures (e.g., Q4 revenue shouldn't be negative or an unreasonable fraction of annual).

• F3.5: Data Integrity & Normalization:

• Implement deduplication logic to ensure each fiscal quarter is represented only once, even if multiple filings reference it.

• Use a robust date comparison function (areInSameQuarter) to accurately align facts (e.g., OCF and Revenue) to the same fiscal quarter, accommodating minor discrepancies in reported endDate values.

• Comprehensive Debt Calculation: If a single "Total Debt" XBRL tag is unavailable, calculate it by summing its common components (e.g., Long-Term Debt Non-Current, Long-Term Debt Current, Short-Term Borrowings, Finance Lease Liabilities).

• F3.6: Unit Conversion: Standardize monetary values (e.g., to millions of USD).

 

5. Key Technical Decisions & Considerations

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• API Strategy:

• SEC API: Utilize a server-side proxy (e.g., Firebase Cloud Function) for all interactions with the SEC EDGAR API. This provides a controlled environment for managing request headers (User-Agent), CIK lookups, and potential future caching or rate limit handling.

• Gemini API: Integrate with Google's Gemini API for AI-driven financial analysis. API key management must be secure (server-side). Design for asynchronous requests to avoid UI blocking, aligning with the "strategic loading screen" feature.

• Data Processing Logic:

• The backend will house the complex logic for fetching, parsing, cleaning, and transforming SEC data. This includes XBRL tag mapping, Q4 calculations, metric derivations (like Total Debt), and data alignment.

• Emphasize robustness in handling missing data or variations in company reporting practices.

• Technology Stack (Proposed):

• Backend/Data Processing: Node.js with TypeScript (recommended for type safety, especially with complex data structures from SEC). Serverless functions (e.g., Firebase Functions, AWS Lambda) are suitable for scalability and managing API proxies.

• Frontend: A modern JavaScript framework like React (with Vite for tooling) for building a dynamic and responsive user interface.

• Charting: A library like ApexCharts or Chart.js for rendering financial data visualizations.

• Error Handling & Logging: Implement comprehensive logging on the backend to trace data extraction and processing steps, aiding in debugging and identifying issues with specific company data. Frontend should clearly communicate errors to the user (e.g., "Could not load data for Ticker XYZ").

• Performance:

• Limit the scope of data processed (e.g., recent filings only) to ensure timely responses.

• Optimize data transformation algorithms.

• Consider future caching mechanisms for frequently accessed company data.

 

6. Design & UX Considerations

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• Simplicity & Clarity: The primary goal is to make complex financial data accessible. Avoid jargon where possible, or provide explanations.

• Speed & Responsiveness: Users expect financial data quickly. The strategic loading for AI insights is a key part of managing this.

• Data Accuracy Perception: Clearly source data (SEC filings) and be transparent about calculations and "N/A" values.

• Visual Hierarchy: Important metrics and AI insights should be prominent.

• Intuitive Navigation: Easy-to-understand tabs and clear data presentation in tables and charts.

 

7. Success Metrics

• User Engagement:

• Daily/Monthly Active Users (DAU/MAU).

• Average session duration.

• Number of companies viewed per session.

• Task Completion & Data Utility:

• User feedback scores (e.g., surveys on data accuracy, usefulness of AI insights).

• Reduction in "N/A" values for key metrics over time (as data handling improves).

• Click-through rates on different data tabs (Financials, Key Metrics, AI Insights).

• System Performance:

• Average page load time for company dashboards.

• API error rates (SEC proxy, Gemini API).

 

8. Future Considerations / Potential Enhancements (Post V1)

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• User accounts for saving favorite companies or watchlists.

• Customizable dashboards and metric selection.

• Email/Push notification alerts for significant financial events or new filings for watched companies.

• Side-by-side company comparison features.

• Integration of more advanced AI analyses (e.g., sentiment analysis from MD&A sections, risk assessment).

• Expansion to include other data sources (e.g., stock price history, analyst estimates, news feeds).

• Downloadable reports (CSV, PDF).

• More granular control over the number of quarters displayed.

• International company data (if feasible with data sources).