SRS Signal

Note: This analysis is based entirely on self-reported information from the project description. No third-party verification, traction data, revenue figures, customer names, or operational history are available.

The description states that SRS Signal is a deterministic demonstration layer built to expose the methodological core of an AI-assisted institutional audit workflow. It includes:

• A four-page web application:
  • Analyze Decision
  • Human Review
  • Reviewed Audit Profile
  • Systemic Signals

Each decision is assessed across seven dimensions:

1. Source traceability
2. Legal-basis specificity
3. Reasoning-chain completeness
4. Counterargument handling
5. Decision-effect justification
6. Correction capacity
7. Overall auditability

The application verifies evidence quotations against exact source text and requires human review for every finding before it contributes to the final result.

It does not produce a democracy score or determine legal correctness; instead, it measures whether reasoning and evidence in a decision are reconstructable and auditable.

Claim: The tool is built using Python, Streamlit, Pydantic v2, pytest, GitHub, and OpenAI Codex/GPT-5.6 for development support.

Inference: The tool is not a live system but a reproducible demonstration with fictional cases.

The author positions SRS Signal as an independent, evidence-based audit mechanism that supports institutional accountability by ensuring decisions can be reconstructed and corrected.

It originates from a research program focused on how institutional representations shape social reality. The project is described as part of an ongoing AI-assisted research programme in Hungary.

Key claims:

• It translates qualitative audit methodology into enforceable software rules.
• It uses AI to assist with document analysis and structuring, but human verification remains mandatory.
• It does not replace official audits or make legal judgments; it focuses on auditability.

Claim: The tool aims to help society observe whether institutions are capable of explaining, documenting, and correcting their own operation.

Inference: This is a positioning statement about societal impact rather than a demonstrated outcome.

The description does not name specific customers or target users. However, it implies that the intended audience includes:

• Judges
• Public institution decision-makers
• Independent auditors
• Researchers studying institutional behavior

It also suggests a broader audience interested in systemic risk detection and institutional accountability.

Claim: The tool is designed for use by judges or external reviewers inspecting institutional decisions.

Inference: No explicit ICP defined; the target user base is inferred from the context of institutional audit.

There is no evidence of a business model, pricing structure, or monetization strategy in the description. The project is described as a demonstration layer for a research programme and does not mention any commercial offering.

Claim: No pricing or revenue model is reported.

Fact: Not evidenced.

The application was built using:

• Python
• Streamlit
• Pydantic v2
• pytest
• GitHub
• OpenAI Codex and GPT-5.6 (for development support)

It uses a deterministic approach, meaning no live API calls or confidential data are involved.

Key technical features:

• Exact-source evidence validation
• Mandatory human review for each dimension
• Transparent recurrence rules
• Aggregation of findings based on thresholds

Claim: The tool does not make live OpenAI API calls and requires no API key.

Fact: Not evidenced — this is self-reported.

There is no evidence of traction, adoption, or operational usage beyond the fictional demonstration. The project is described as a Build Week submission to an OpenAI hackathon and is explicitly stated to be a deterministic demonstration layer.

Claim: The tool has not been used operationally outside of its demo.

Fact: Not evidenced — this is self-reported.

The description does not provide any information about competitors or existing tools in the space of institutional audit, accountability, or systemic risk detection. It does not reference similar products or platforms.

Claim: No competitive landscape described.

Fact: Not evidenced.

1. Lack of operational use: The tool is presented only as a demonstration; no evidence of real-world deployment or institutional adoption.
2. Unverified AI role: While AI is mentioned, there's no clarity on how it’s used in practice beyond development support.
3. No commercial viability: No pricing, revenue model, or customer base described.
4. Limited scope: The demonstration uses only fictional cases and does not scale to real-world institutional complexity.

Inference: These are risks due to lack of evidence for operationalization or scalability.

1. Has the methodology been tested in real institutions, and if so, what were the outcomes?
2. What is the actual role of AI in the broader research workflow vs. this demo layer?
3. Are there plans to integrate live data or models into the tool beyond the current deterministic version?
4. How does the human review process scale across multiple users or institutions?
5. Is there any feedback from judges, auditors, or researchers who have used the system?
6. What are the legal and ethical considerations around auditing public decisions?