AuditGuard DE

Note: This analysis is based solely on the self-reported, unverified project description provided. No independent verification, traction data, revenue figures, or customer information are available.

The description states that AuditGuard DE is an “interactive, audit-first prototype” for German EÜR and SKR03 workflows. It registers four synthetic source reports—EXTF, SuSa, BWA and USt-VA—and reconciles revenue, operating costs, profit and input VAT.

It enforces a "HARD STOP" when material differences are detected (e.g., EUR 190.00 in input VAT), without estimating or reconstructing missing values. It identifies the missing evidence and withholds final results until all control differences reach zero.

The system is built using React, TypeScript, Next.js, Vite, and uses Codex and GPT-5.6 for development, review and validation.

Inference: The product appears to be a proof-of-concept or demonstration tool, not a production-ready solution. It is described as deterministic and repeatable, with explicit stopping conditions.

The author positions AuditGuard DE around the principle of “evidence before inference.” This is presented as a core value that distinguishes it from systems that guess missing values or allocate VAT proportionally.

The system is framed as a way to avoid errors caused by unsupported assumptions, unauthorized reconstructions, and guessing. It is described as a method for encoding expert knowledge into explicit rules rather than relying on generative AI alone.

Claim: The system avoids guessing missing VAT or allocating it proportionally.

Inference: This positioning reflects an attempt to address audit risk in German accounting workflows by emphasizing transparency, traceability and compliance over convenience or speed.

The description does not clearly identify a specific customer segment. However, the system is designed for use within German EÜR (Einnahmen- und Ausgabenrechnung) and SKR03 (Standardkontenrahmen 2003) workflows.

It targets users who work with synthetic or real accounting reports in these formats, particularly those involved in tax audits or compliance processes where accuracy is critical.

Claim: The system supports EXTF, SuSa, BWA and USt-VA reports.

Inference: Based on the context of German accounting standards, potential users may include small-to-medium enterprises (SMEs), accountants or auditors working in Germany.

There is no evidence of a business model or pricing structure. The project is described as a public demo and prototype submitted to a hackathon.

Claim: No pricing, monetization or business model details are provided.

Inference: This suggests the project is not yet commercialized or monetized.

The system was built using:

• Frameworks: React, Next.js, TypeScript, Vite
• AI tools: Codex, GPT-5.6
• Deployment: OpenAI Sites
• Interface: Bilingual (English/Polish)

It uses a deterministic workflow to ensure decisions are visible, repeatable and verifiable.

Claim: The system is built with React, TypeScript, Next.js, Vite.

Inference: These technologies suggest a modern web-based prototype, likely intended for demonstration purposes rather than enterprise deployment.

The project is described as a "prototype" and a "demo". It includes downloadable synthetic CSV files and a live interface but lacks any indication of adoption or usage beyond the demo.

Claim: The system is a prototype, not a production-ready tool.

Inference: No evidence of customer base, revenue, or real-world implementation is provided. The project appears to be in early-stage development.

The description does not mention competitors or existing tools in the German accounting reconciliation space. It focuses on the unique approach of refusing to guess missing values and enforcing strict evidence-based reconciliation.

Claim: No competitive landscape is described.

Inference: The lack of competitor references suggests either limited awareness of existing solutions or that this is a niche or emerging area.

• Prototype-only status: The system is described as a demo and prototype, with no evidence of real-world adoption or integration.
• No commercialization: No pricing, monetization or business model details are provided.
• Limited scope: It only supports synthetic data and does not yet handle document ingestion or OCR.
• Unverified claims: The author’s own account is self-reported and unverified.

Inference: Without traction, customer feedback, or integration into real systems, the project may be more of a technical experiment than a viable product.

1. Has this prototype been tested with actual users or auditors?
2. Are there plans to integrate real document ingestion or OCR capabilities?
3. What is the roadmap for moving from prototype to production-ready system?
4. How does the system handle edge cases not covered in the demo?
5. Is there any interest from accounting firms or tax professionals in adopting this approach?
6. Has the author considered regulatory compliance beyond the demonstration?