AJA is not a collection of financial tools. It is a structured financial intelligence architecture — three integrated layers that diagnose, normalize, and maintain the integrity of financial statement data for owners, investors, and buyers.
Every financial statement submitted to the AJA platform moves through a structured intelligence workflow — from raw document to structured, validated, decision-ready output. This is the core infrastructure that makes AJA a platform, not a point solution.
Proprietary Financial Intelligence Workflow — SaaS self-service or full managed service delivery via COH's U.S. and India delivery team
Each layer addresses a distinct stage in the financial data lifecycle. Together they form a complete, end-to-end financial intelligence architecture — from initial diagnostics through structured output and ongoing integrity monitoring.
Initial financial intelligence extraction and validation
Financial normalization and classification engine
Hybrid AI + Managed Financial Infrastructure
The difference between a tool and infrastructure is durability, integration, and compounding value. AJA is designed to deliver measurable business outcomes — not just diagnostic outputs.
Every product operates as a component of the unified financial intelligence stack. See the complete platform documentation at COH's Platform & Products page →
| Product | Layer | Status | Description |
|---|---|---|---|
| BalanceSheet AI | Diagnostics | Phase 1 Live | Automated balance sheet review — IRS category mapping, discrepancy detection, investor-grade output. Details ↗ |
| EBITDA Quick Check | Diagnostics | Live — Free | Free EBITDA validation with add-back identification. No signup. Launch → |
| P&L vs Balance Sheet Cross Validator | Diagnostics | Planned | Cross-document validation detecting discrepancies between income statements and balance sheets. |
| IRS Category Mapper | Normalization | Planned | AI-driven expense-to-IRS-category mapping for clean chart-of-accounts structuring. |
| Business vs Non-Business Classifier | Normalization | Planned | Automated identification and separation of owner-personal expenses from operational business expenses. |
| Detailed EBITDA Analyzer | Normalization | In Development | Multi-year EBITDA trend analysis with normalization recommendations and defensible schedule output. |
| Financial Statement Remediation | Remediation | In Development | Flat-fee managed service for correcting, restructuring, and validating financial statements — delivered by COH's delivery team. |
| AI Monthly Financial Maintenance | Remediation | Planned | Continuous financial integrity monitoring — categorization surveillance, anomaly alerting, and ongoing validation. |
Platform infrastructure compounds in value in ways that individual tools do not. As the AJA platform processes financial data across a broader range of business structures, industries, and statement formats, the underlying frameworks that drive diagnostics and normalization become more precise and more comprehensive. This is not a claim about machine learning — it is a characteristic of any well-structured analytical process that encounters more cases over time.
The structured taxonomy of financial statement discrepancy types — organized by category, severity, and diligence impact — expands as new patterns are encountered and documented. A more complete taxonomy means broader and more reliable diagnostic coverage.
Expense categorization rules are continuously reviewed and refined against IRS guidance, accounting standards, and edge cases from real-world platform use. Categorization logic that has been tested against a wider range of inputs is more accurate and more defensible than logic that has not.
Financial statement structures vary by industry. Service businesses, retail, manufacturing, and professional services carry different cost profiles, revenue recognition patterns, and normalization requirements. Industry-specific mapping frameworks become more nuanced as the platform encounters more examples within each segment.
The financial normalization methodology becomes more formally structured over time — with clearer documentation standards for add-backs, more consistent conventions for owner compensation adjustment, and more defined criteria for non-recurring item classification. Process maturity directly improves output consistency.
This refinement model is one of the structural advantages of building financial intelligence as infrastructure rather than as isolated tools. See the full strategic context: Financial Data Infrastructure Strategy →
No signup. No data retention. Run an EBITDA diagnostic in minutes — the starting point for structured financial validation.