# WACCY MISSION

## Core Mission

WACCY is an intelligent financial modeling platform designed to automatically extract, parse, classify, and synthesize business data from diverse sources to generate sophisticated, production-grade financial models and operating analyses. **The platform's primary focus is on small businesses—from sole proprietorships to growing companies—that struggle with messy, incomplete, and poorly-maintained financial records.** While WACCY is not intentionally designed to exclude large enterprises, the reality is that small businesses face unique challenges: inconsistent record-keeping, ambiguous account classifications, incomplete data, and limited financial infrastructure. WACCY transforms this raw, often chaotic business data into comprehensive, auditable, and decision-ready financial models that adhere to institutional-quality standards.

The fundamental challenge WACCY addresses is the ambiguity and incompleteness that poor record-keeping creates in small business financial data. Unlike large enterprises with dedicated accounting teams and rigorous financial controls, small businesses often have:
- Inconsistent account naming and categorization
- Missing or incomplete transaction records
- Mixed personal and business expenses
- Unclear revenue recognition and expense classification
- Limited historical data and documentation
- Manual processes prone to errors and omissions

WACCY leverages large language models (LLMs) and foundation models as intelligent agents for parsing, classification, and data extraction, while maintaining a preference for deterministic functions and rule-based models wherever possible to ensure accuracy, reproducibility, and auditability. The platform's architecture prioritizes transparency, allowing users to understand the provenance of every data point and the logic behind every calculation, even when working with incomplete or ambiguous source data.

## Data Sources and Integration Strategy

### Core Data Sources

WACCY maintains a simple, focused core with two primary data source integrations, each serving a distinct purpose in addressing the small business financial modeling challenge:

- **QuickBooks Online (QBO)**: Direct API integration to extract chart of accounts, general ledger entries, financial statements (income statement, balance sheet, cash flow statement), transaction-level detail, vendor and customer data, and supporting schedules. **QBO is the primary data source because it is the accounting system most commonly used by small businesses.** While QBO provides structured data through its API, the reality is that small businesses often maintain their QuickBooks records inconsistently, with ambiguous account names, misclassified transactions, and incomplete entries. WACCY must intelligently parse and normalize this messy data, treating source classifications with appropriate skepticism and mapping everything to the standardized WACCY chart of accounts.

- **SEC EDGAR**: Automated parsing of 10-K, 10-Q, 8-K filings, proxy statements, and registration statements for publicly traded companies. **EDGAR serves a dual purpose**: First, it provides an easy source of truth—high-quality, professionally-prepared financial data from the largest businesses, where tabular financial data is associated with natural language reports written by accounting and finance professionals. Second, and more importantly, EDGAR provides access to thousands and thousands of cases that demonstrate how financial events, transactions, and business activities are properly classified, reported, and explained. This corpus of high-quality examples enables WACCY to learn patterns and causal chains—how revenue recognition works, how expenses are categorized, how working capital moves, how transactions flow through financial statements—that can then be applied to infer proper classification and modeling approaches for similar situations in small businesses, even when their records are incomplete or ambiguous. By studying how professionals at large companies handle complex financial scenarios, WACCY can apply those same principles to help sole proprietorships and small businesses achieve similar analytical rigor despite their messy source data.

### Modular Data Source Extensions

All other data sources are implemented as modular, extensible packages that plug into the core WACCY platform. This design philosophy ensures:

- **Simplicity**: The core platform remains focused and maintainable
- **Extensibility**: New data sources can be added without modifying core functionality
- **Flexibility**: Users install only the data source integrations they need
- **Community Development**: Third-party developers can create and maintain data source packages

Examples of modular data source packages (not included in core):
- **Google Drive / Gmail**: Document parsing and email extraction for unstructured data
- **Banking and payment systems**: Transaction-level data from bank feeds and payment processors
- **CRM and sales systems**: Pipeline data, customer contracts, and revenue recognition details
- **HR and payroll systems**: Headcount data and compensation structures
- **ERP and inventory systems**: Supply chain data and operational metrics
- **Market data APIs**: Real-time pricing, trading multiples, and economic indicators
- **Other accounting systems**: Xero, Sage, NetSuite, and other accounting platform integrations

The core platform provides a standardized interface and ontology that all data source packages must conform to, ensuring consistent data classification and model construction regardless of the source.

### Data Quality and Parsing Philosophy

WACCY employs a hybrid approach to data extraction and classification, designed specifically to handle the messy, incomplete data typical of small businesses:

1. **Deterministic First (Core)**: The core platform uses deterministic parsing functions and rule-based extraction where data structures are known:
   - **QuickBooks Online**: While QBO provides structured API access, the platform does not blindly trust source account classifications. Instead, it uses deterministic mapping combined with intelligent validation to map messy, inconsistently-named accounts to the WACCY standard chart of accounts. The platform recognizes that small businesses often have poorly-organized charts of accounts and treats source classifications with appropriate skepticism.
   - **SEC EDGAR**: Standardized filing formats enable deterministic parsing of financial statements and disclosures. This high-quality data serves as both a reference source and a training corpus for understanding proper financial classification and reporting patterns.

2. **LLM-Enhanced Parsing for Ambiguity Resolution**: The platform leverages foundation models to handle the ambiguity and incompleteness inherent in small business data:
   - **Account Classification**: When source account names are ambiguous or don't clearly map to standard categories, LLMs analyze transaction patterns, account relationships, and contextual clues to infer proper classification
   - **Missing Data Inference**: When data is incomplete, the platform uses patterns learned from EDGAR filings and other high-quality sources to infer what might be missing and how to handle gaps
   - **Causal Chain Recognition**: By studying thousands of EDGAR filings, the platform learns how financial events cascade through statements (e.g., how a lease affects both income statement and balance sheet), enabling it to identify and correct similar patterns in messy small business data
   - **Terminology Normalization**: Recognizing equivalent concepts across different naming conventions (e.g., "revenue," "sales," "income") and mapping them to standardized accounts
   - **Relationship Inference**: Linking related data points (e.g., connecting a lease agreement mentioned in notes to balance sheet lease liabilities) even when explicit connections are missing

3. **Validation and Reconciliation**: All extracted data—whether from core sources or modular extensions—undergoes validation checks, cross-referencing between sources, and reconciliation to ensure consistency. The platform flags discrepancies, missing data, ambiguous classifications, and potential errors for user review. All data sources, regardless of origin or quality, must map to the standardized WACCY chart of accounts, enabling quality quantification even when source data is messy.

### Data Classification Ontology and Standardized Chart of Accounts

A fundamental principle of WACCY is the establishment and maintenance of a standardized classification ontology that serves as the authoritative baseline for all financial data mapping and model construction. This ontology is critical because the quality and comparability of all downstream analysis—from basic financial statements to sophisticated valuation models—depends entirely on consistent, accurate classification of financial data.

**The WACCY Standardized Chart of Accounts**

WACCY maintains a comprehensive, standardized chart of accounts that serves as the canonical reference for all financial data classification. This standard chart of accounts is designed to:

- **Ensure Consistency**: All businesses, regardless of their source system's account structure, are mapped to the same standardized accounts, enabling consistent analysis, benchmarking, and comparison
- **Support All Model Types**: The standard chart of accounts is structured to support the full range of model types—from basic 3-statement models to complex LBO and M&A analyses—with proper account hierarchies and classifications
- **Enable Quality Quantification**: By standardizing classifications, WACCY can quantify the quality and completeness of data extraction, mapping accuracy, and model outputs
- **Facilitate Cross-Company Analysis**: Standardized accounts enable meaningful peer comparisons, industry benchmarking, and transaction comparables analysis

The WACCY standard chart of accounts includes:
- **Income Statement Accounts**: Revenue (by type and segment), COGS (by component), operating expenses (by functional area), non-operating items, taxes, and non-recurring items
- **Balance Sheet Accounts**: Current and non-current assets (with working capital detail), current and non-current liabilities (including debt detail), equity components, and off-balance sheet items
- **Cash Flow Accounts**: Operating, investing, and financing activities with detailed line items
- **Supporting Schedules**: Debt schedules, equity schedules, working capital detail, and other supporting analyses

**Skeptical Treatment of Input Classifications**

WACCY treats all input chart of accounts and classifications from source systems with appropriate skepticism. Rather than blindly accepting account names, categories, and classifications from QuickBooks, Google Drive documents, or other sources, the platform:

- **Normalizes All Input**: Every account from source systems is mapped to the WACCY standard through intelligent classification, not direct import
- **Validates Classifications**: LLM-enhanced classification validates that source account names and categories align with their actual usage and transaction patterns
- **Flags Anomalies**: Accounts that don't map cleanly to the standard, have ambiguous classifications, or show inconsistent usage patterns are flagged for review
- **Learns from Context**: Classification considers not just account names but transaction patterns, relationships to other accounts, and contextual clues from financial statements

This skeptical approach is essential because:
- **Source Systems Are Inconsistent**: Businesses use wildly different account naming conventions, categorization schemes, and organizational structures
- **Errors Are Common**: Manual account setup, copy-paste errors, and evolving business needs lead to misclassified accounts in source systems
- **Quality Depends on Accuracy**: A single misclassified account can cascade through all downstream analysis, making it impossible to quantify model quality
- **Comparability Requires Standardization**: Meaningful analysis across companies, time periods, or scenarios requires consistent classification

**Industry-Specific Customization from Standard**

While the WACCY standard chart of accounts serves as the universal baseline, the platform supports industry-specific extensions and customizations that build upon—rather than replace—the standard:

- **Industry Templates**: Pre-configured extensions for SaaS, manufacturing, retail, real estate, professional services, and other industries that add industry-specific accounts while maintaining the core standard
- **Custom Account Hierarchies**: Businesses can add custom accounts and sub-accounts for industry-specific needs (e.g., "Deferred Revenue - Annual Subscriptions" for SaaS companies) that map to standard parent categories
- **Flexible Mapping**: The platform maintains the mapping between custom/industry-specific accounts and the standard, ensuring that custom accounts enhance rather than fragment the standard ontology
- **Backward Compatibility**: All customizations maintain compatibility with the standard, ensuring that industry-specific models can still be compared and analyzed using standard metrics

**Quality Quantification Through Standardization**

The standardized ontology enables WACCY to quantify the quality of data extraction, classification, and model outputs:

- **Mapping Confidence Scores**: Each account mapping receives a confidence score based on the clarity of the mapping, consistency of usage, and validation against transaction patterns
- **Completeness Metrics**: The platform can measure how completely a business's financial data maps to the standard, identifying gaps and missing information
- **Classification Accuracy**: By comparing source classifications to validated mappings, WACCY can assess and report on classification accuracy
- **Model Quality Indicators**: Standardized inputs enable quality metrics for model outputs—balance checks, ratio reasonableness, trend consistency, and peer comparability
- **Audit Trails**: Every classification decision is logged, allowing users to understand how source accounts were mapped to the standard and review any questionable mappings

This quality quantification is only possible because of the standardized ontology. Without a consistent baseline, it becomes impossible to determine whether a model's outputs are accurate, complete, or comparable—rendering sophisticated analysis meaningless regardless of the sophistication of the modeling techniques employed.

## Financial Modeling Capabilities

WACCY generates institutional-quality financial models that encompass the full spectrum of financial analysis, from basic operating models to sophisticated valuation and transaction analysis. The platform adheres to professional standards for model architecture, formula design, and presentation.

### Model Engineering and Spreadsheet Craft

All models output to multiple formats, with Google Sheets serving as the primary spreadsheet platform. Models are constructed with:

- **Model Architecture**: Modular tab structures that separate inputs/assumptions from calculations and outputs, consistent time axis across all statements, clear sign conventions, and isolation of hardcoded values to designated assumption areas
- **Formatting Standards**: Professional color conventions (inputs in blue, calculations in black, outputs in green), consistent unit handling (thousands/millions), robust date handling, and print-ready layouts
- **Formula Design**: Robust cell links that avoid fragile OFFSET dependencies, transparent logic that can be easily audited, and minimization of circular references with controlled iterative calculation where necessary
- **Speed and Scale**: Efficient formula construction, optimized calculation settings, iterative calculation controls, and performance hygiene for models handling large datasets
- **Auditability**: Built-in check rows, balance checks, roll-forward reconciliations, error flags, reconciliation tables, and support for tracing precedents and dependents
- **Scenario Tooling**: Data tables for sensitivity analysis, scenario toggles, switch cases for multiple scenarios, goal seek and solver integration, sensitivity matrices, and tornado charts for risk analysis
- **Advanced Spreadsheet Tools**: Integration with pivot tables, named ranges, dynamic arrays (ARRAYFORMULA/LAMBDA in Google Sheets), query functions, and Apps Script automation capabilities

### Accounting and Financial Statement Analysis

Models incorporate deep understanding of accounting mechanics:

- **Income Statement Mechanics**: Proper revenue recognition principles, accurate COGS vs operating expense classification, margin stack analysis, stock-based compensation handling, and identification of non-recurring items
- **Balance Sheet Mechanics**: Working capital account modeling (accounts receivable, inventory, accounts payable), deferred revenue recognition, accrued expense tracking, proper debt vs equity classification, and lease accounting (ASC 842) awareness
- **Cash Flow Mechanics**: Indirect method cash flow construction, non-cash add-backs, net working capital bridge analysis, proper capex vs opex classification, and accurate financing vs investing activity categorization
- **Quality of Earnings**: Normalization adjustments to identify run-rate performance, separation of one-time costs and restructuring charges, foreign exchange impact analysis, and adjustments for non-operating items
- **Ratio and Returns Analysis**: Calculation of ROIC, ROE, ROA, DuPont analysis, margin and asset turnover metrics, leverage ratios, coverage ratios, and liquidity measures
- **Credit Analysis Lens**: Leverage ratio calculations, fixed-charge coverage analysis, covenant compliance modeling, and debt capacity assessment frameworks

### Forecasting Methods (Driver-Based, Not Assumption-Based)

Models employ sophisticated forecasting methodologies that build from operational drivers rather than top-down assumptions:

- **Revenue Builds**: Price × volume models, units × ARPU (average revenue per user) frameworks, cohort and retention models for SaaS businesses, pipeline conversion models, and capacity-based revenue projections
- **Cost Builds**: Variable vs fixed cost frameworks, detailed headcount models with compensation structures, gross margin driver analysis, operating expense builds by functional area, and inflation indexing
- **Working Capital Modeling**: Days sales outstanding (DSO), days inventory outstanding (DIO), days payable outstanding (DPO) analysis, working capital as percentage of revenue, seasonality adjustments, inventory turnover analysis, and deferred revenue dynamics
- **Capex & Depreciation**: Capital expenditure modeling by category, asset life assumptions, depreciation waterfall construction, and capitalized software development cost handling
- **Debt & Interest Modeling**: Revolver mechanics with availability calculations, fixed and floating rate modeling, spread analysis, detailed amortization schedules, and cash sweep waterfall construction
- **Equity & Share Modeling**: Basic and diluted share count calculations, treasury stock method for options, convertible security handling, RSU and option modeling, and share buyback/issuance tracking
- **Tax Modeling**: Effective tax rate vs statutory rate analysis, deferred tax assets (DTAs) and deferred tax liabilities (DTLs) modeling, NOL (net operating loss) utilization tracking, and interest limitation (Section 163(j)) awareness
- **Segment and Geography Analysis**: Segment-level KPI tracking, mix shift analysis, foreign exchange translation handling, and margin difference analysis by segment

### Core Valuation Methods

WACCY generates comprehensive valuation analyses using industry-standard methodologies:

- **DCF (Discounted Cash Flow) Intrinsic Valuation**
  - Unlevered free cash flow construction (NOPAT + D&A − Capex − ΔNWC)
  - Proper discounting mechanics including stub period handling and mid-year convention
  - Terminal value calculation using both perpetuity growth and exit multiple methods
  - Enterprise value to equity value bridge accounting for net debt, minority interests, investments, and preferred equity
  - WACC (weighted average cost of capital) build including cost of equity (CAPM), cost of debt, and target capital structure assumptions

- **Relative Valuation**
  - Trading comparables analysis with peer selection, financial statement spreading, metric normalization, and calculation of EV/Revenue, EV/EBITDA, P/E, and sector-specific multiples
  - Transaction comparables analysis using precedent M&A deals, implied multiple calculation, control premium analysis, and deal-specific adjustments

- **Additional Valuation Frameworks**
  - Sum-of-the-parts (SOTP) valuation for diversified businesses
  - Implied multiple triangulation across methodologies
  - Football field valuation summaries
  - APV (Adjusted Present Value) for clarity on leverage and tax shield effects
  - Residual income and economic profit frameworks where applicable

### M&A and Pro Forma Analysis Methods

For transaction analysis, models include:

- **Accretion/Dilution Analysis**: Pro forma EPS impact calculation, synergy timing and phasing, financing mix sensitivity analysis
- **Sources & Uses of Funds**: Cash, stock, and debt mix analysis, transaction fees, refinancing assumptions, and rollover equity calculations
- **Purchase Accounting / PPA (Purchase Price Allocation)**: Goodwill calculation, intangible asset identification and valuation, step-up adjustments, deferred tax impacts, and amortization schedule construction
- **Deal Structure Modeling**: Fixed vs floating exchange ratio analysis, collar mechanisms, contingent consideration and earn-out modeling
- **Synergy Modeling**: Cost synergy and revenue synergy identification, phasing assumptions, one-time integration cost tracking, and dis-synergy recognition
- **Contribution Analysis**: Relative ownership calculations, value transfer analysis, and synergy split logic
- **Pro Forma Financial Statements**: Combined income statement, balance sheet, and cash flow statement with transaction adjustments and financing effects

### LBO and Private Equity Modeling Methods

For leveraged buyout and private equity analysis:

- **Returns Math**: IRR (internal rate of return), MOIC (multiple on invested capital), and cash-on-cash return calculations
- **Capital Structure Modeling**: Senior debt, mezzanine debt, high-yield notes, PIK (payment-in-kind) instruments, revolver facilities, management rollover equity, and preferred equity structures
- **Debt Schedule Construction**: Mandatory amortization schedules, cash sweep mechanics, refinancing assumptions, and call protection awareness for high-yield debt
- **Interest Modeling**: Fixed vs floating rate handling, SOFR/LIBOR base rate + spread calculations, blended interest rate determination, and average balance conventions
- **Tax Effect Analysis**: Interest tax shield calculations, NOL utilization, interest limitation awareness, and cash tax vs book tax reconciliation
- **Exit Analysis**: Exit multiple scenarios, IPO vs strategic sale vs secondary sale analysis, deleveraging effects, and dividend recapitalization modeling
- **Sensitivity Analysis**: Comprehensive sensitivity grids analyzing entry multiple, exit multiple, leverage levels, EBITDA growth, margin expansion, hold period, and interest rate environment impacts

### Data Sourcing, Standardization, and "Spreading"

The platform handles the complex work of data normalization and standardization:

- **Core Source Integration**: Automated extraction from SEC EDGAR filings (10-K, 10-Q, 8-K, proxy statements) for public company data. Additional sources (earnings releases, investor presentations, call transcripts) can be provided through modular extensions.
- **Normalization and Adjustments**: GAAP to non-GAAP reconciliation, run-rate adjustments, segment restatement handling, and pro forma adjustment identification
- **Consistency Work**: Calendarization of fiscal periods to calendar quarters, LTM (last twelve months) building, pro forma adjustment application, and currency and foreign exchange handling
- **Market Data Integration**: Market data integration (share price, market capitalization, debt pricing, yield curves) can be provided through modular extension packages, ensuring the core platform remains focused

### Presentation and Decision-Support Outputs

Models generate professional outputs designed for decision-making:

- **Valuation Summaries**: Valuation ranges with quartiles and medians, implied valuation calculations, and key assumption callouts
- **Charts & Tables**: Comparables tables, sensitivity analysis tables, bridge charts (enterprise value to equity value), debt waterfall diagrams, and return bridge analyses
- **Narrative Framing**: Investment highlights, key risk identification, diligence question generation, and KPI dashboard construction

## Model Types and Deliverables

WACCY generates a comprehensive suite of financial model types:

- **3-Statement Integrated Operating Model**: The foundational model type, integrating income statement, balance sheet, and cash flow statement with full balancing and reconciliation
- **DCF Valuation Model**: Discounted cash flow analysis with detailed free cash flow construction, terminal value analysis, and WACC calculation
- **Trading Comparables Model**: Peer company analysis with multiple calculation and benchmarking
- **Transaction Comparables Model**: Precedent M&A transaction analysis
- **Merger Model**: Accretion/dilution analysis combined with purchase accounting
- **LBO Model**: Leveraged buyout analysis with returns calculation and debt schedule modeling
- **SOTP / Break-Up Valuation Model**: Sum-of-the-parts valuation for diversified businesses
- **Cap Table & Dilution Model**: Equity ownership tracking with options, convertibles, and dilution analysis
- **Credit / Covenant Compliance Model**: Debt covenant tracking and compliance analysis
- **Budget vs Actual / Forecast (FP&A) Model**: Financial planning and analysis with variance analysis
- **Project Finance / Infrastructure Model**: Cash waterfall construction with DSCR (debt service coverage ratio) and LLCR (loan life coverage ratio) analysis
- **Real Estate (REIT) / Property Cash Flow Model**: Property-level cash flow and valuation analysis
- **SaaS Cohort + Unit Economics Model**: Subscription business analysis with cohort retention and unit economics
- **Carve-Out / Spin / Restructuring Model**: Corporate restructuring and separation analysis

## Phased Development Approach

### Phase One: Core Foundation and 3-Statement Models

**Objective**: Establish the foundational platform with core data source integration, standardized ontology, and basic operating model generation—specifically designed to handle the messy, incomplete data typical of small businesses using QuickBooks Online.

**Deliverables**:
- Core platform architecture with modular data source interface
- QuickBooks Online API integration (core data source) with intelligent handling of ambiguous, inconsistently-named accounts
- WACCY standardized chart of accounts definition and implementation
- Intelligent mapping of messy source chart of accounts to WACCY standard (with LLM-enhanced classification and validation to handle poor record-keeping)
- Automated 3-statement financial model generation (income statement, balance sheet, cash flow statement) using standardized accounts, even when source data is incomplete
- Basic model architecture with proper balancing, reconciliation, and audit trails
- Standard formatting and presentation outputs
- Mapping confidence scoring and classification quality metrics to quantify data quality despite source ambiguity
- Plugin architecture for modular data source extensions

**Technical Focus**: Reliability, accuracy, and auditability of data extraction from messy small business records, standardized classification ontology that handles ambiguity, and model construction that works with incomplete data. Establishing the foundation for quality-quantifiable analysis through consistent data classification and a simple, extensible architecture that doesn't require perfect source data.

### Phase Two: Public Market Data and Pattern Learning

**Objective**: Integrate SEC EDGAR filings as both a source of high-quality reference data and a training corpus for learning financial classification patterns that can be applied to messy small business data.

**Deliverables**:
- SEC EDGAR integration (core data source) for public company data extraction
  - 10-K, 10-Q, 8-K automated parsing
  - Proxy statement analysis
  - Registration statement processing
- Pattern learning framework that extracts causal chains and classification patterns from thousands of EDGAR filings
- Application of learned patterns to infer proper classification and modeling approaches for similar situations in small businesses
- Trading comparables model generation
- Transaction comparables model construction
- Data normalization and calendarization frameworks
- Cross-company analysis capabilities

**Technical Focus**: Large-scale data normalization, calendarization, pattern extraction from high-quality professional financial reports, and applying those patterns to improve classification of messy small business data. Using EDGAR's combination of tabular data and natural language reports to understand how professionals handle complex financial scenarios.

### Phase Three: Advanced Valuation Models

**Objective**: Enable comprehensive valuation analysis through sophisticated model types.

**Deliverables**:
- DCF valuation model automation
- M&A and pro forma analysis models
- LBO model generation
- Advanced valuation framework integration
- Sensitivity and scenario analysis tooling

**Technical Focus**: Sophisticated model type generation and valuation methodology implementation.

### Phase Four: Specialized Model Types

**Objective**: Expand to specialized model types for specific use cases and industries.

**Deliverables**:
- SaaS cohort and unit economics models
- Real estate and REIT models
- Project finance and infrastructure models
- Cap table and dilution analysis
- Credit and covenant compliance models
- Industry-specific template libraries (as modular extensions)

**Technical Focus**: Domain expertise integration and specialized calculation frameworks.

### Phase Five: Advanced Analysis and Decision Support

**Objective**: Enable sophisticated scenario modeling and strategic decision support.

**Deliverables**:
- Multi-scenario model generation
- Sensitivity analysis automation
- Monte Carlo simulation capabilities
- Strategic planning model integration
- What-if analysis tools
- Automated scenario comparison and reporting
- Intelligent data synthesis from fragmented sources
- Model generation from incomplete or inconsistent data with confidence scoring

**Technical Focus**: Advanced modeling capabilities, decision-support tooling, and robustness in handling data quality issues.

### Modular Extension Development

Throughout all phases, the platform architecture supports modular data source extensions developed as separate packages. These extensions can be developed by the core team or by the community, and include:

- Document parsing packages (Google Drive, PDFs, spreadsheets)
- Communication extraction packages (Gmail, Slack, etc.)
- Banking and payment system integrations
- CRM and sales system integrations
- HR and payroll system integrations
- ERP and inventory system integrations
- Market data API integrations
- Other accounting system integrations (Xero, Sage, NetSuite, etc.)

All extensions conform to the core platform's standardized interface and ontology, ensuring consistent data classification and model construction regardless of the source.

## Design Principles

1. **Simplicity and Focus**: The core platform maintains a simple, focused design with only essential data source integrations (QBO and EDGAR). All other functionality is provided through modular, extensible packages that plug into the core.

2. **Standardized Ontology First**: All financial data is mapped to a standardized WACCY chart of accounts, regardless of source system classifications. Input classifications are treated skeptically and normalized to ensure consistency, comparability, and quantifiable quality across all analyses.

3. **Modular Extensibility**: The platform architecture is designed for extensibility, not comprehensiveness. New data sources, model types, and analysis frameworks are added as separate packages that conform to core interfaces, not as monolithic additions to the core platform.

4. **Accuracy First**: Deterministic functions preferred over probabilistic models where possible. LLMs used for parsing and classification, not for financial calculations.

5. **Transparency and Auditability**: Every data point traceable to source, every calculation explainable, every assumption documented, and every classification mapping reviewable.

6. **Professional Standards**: Models adhere to institutional-quality standards for architecture, formatting, and presentation.

7. **Quality Quantification**: The standardized ontology enables measurement and reporting of data quality, classification accuracy, and model output reliability—without standardization, quality cannot be meaningfully assessed.

8. **Scalability**: Platform designed primarily for small businesses—from sole proprietorships to growing companies—with the capability to handle larger enterprises. The focus is on making sophisticated financial modeling accessible to businesses that lack dedicated finance teams and rigorous accounting infrastructure.

9. **User Empowerment**: Users maintain control over assumptions, can override automated extractions and classifications, and can customize model outputs while benefiting from automation. Users install only the data source packages they need.

## Long-Term Vision

WACCY aims to become the standard platform for automated financial modeling for small businesses, enabling entrepreneurs, small business owners, investors, and advisors to generate sophisticated financial analyses with minimal manual effort—even when source data is messy, incomplete, or poorly maintained. By intelligently synthesizing data from diverse sources and learning from high-quality examples in EDGAR filings, the platform democratizes access to institutional-quality financial modeling capabilities for businesses that lack dedicated finance teams.

The ultimate goal is to enable small business owners and their advisors to answer complex financial questions quickly and accurately: What is this business worth? How would a transaction structure impact returns? What are the key risks and sensitivities? How does this company compare to its peers? By automating the tedious work of data extraction, normalization of messy records, and model construction—while learning from thousands of high-quality examples of how large companies handle similar financial scenarios—WACCY allows users to focus on strategic analysis and decision-making rather than wrestling with incomplete QuickBooks records and ambiguous account classifications.
