Chattanooga Animal Radar

Project: Chattanooga Animal Radar

Author's self-description: An animal-first Chattanooga routing prototype that turns fictional reports into evidence-aware next steps without overstating provider authority, capacity, or outcomes.

Team size: 1

Technology stack: CSS3, GPT-5.6, HTML5, JavaScript, OpenAI, Python, Responses

Context: Submitted to the OpenAI 2026 hackathon on Devpost

This is a self-reported prototype project built during a hackathon. The description states it is a routing tool for animal welfare reports in Chattanooga, designed to avoid overstating authority or outcomes by using fictional data and structured outputs from GPT-5.6. It does not claim revenue, customers, or traction beyond its own demonstration.

Key Insight: The project appears to be an experimental system that separates language understanding from authority assignment, using a deterministic gate to prevent overstatement in routing decisions. It is built with a focus on evidence transparency and accessibility.

Most Important Open Question: What is the actual scope of the routing logic, and how does it map to real-world animal-control jurisdictions? The project claims to use "reviewed routing research" but provides no details about that research or its applicability beyond fictional examples.

The description states that Chattanooga Animal Radar is a routing prototype for animal welfare reports in Chattanooga, Tennessee. It uses fictional narratives as input and produces one of three outcomes:

• A verified first contact for the City of Chattanooga;
• A withheld route for Ridgeside due to conflicting authoritative sources; or
• A narrow published lead for Signal Mountain that is not broadened into a full animal-control pathway.

It includes:

• An HTML, CSS, and JavaScript interface;
• A pure JavaScript routing core shared by UI and tests;
• A loopback-only Python server with an allowlisted static surface;
• Strict schema-constrained extraction via OpenAI Responses API;
• Deterministic claim-state gates and provenance-preserving evidence receipts.

The system is described as non-operational — it does not collect, store, or transmit real animal reports. It uses fictional data only and is intended to demonstrate how routing decisions can be made without overstating authority or outcomes.

Inference: The product is a proof-of-concept for a routing engine that separates natural language processing from jurisdictional assignment, using structured outputs and deterministic logic to avoid false certainty.

The description states that the project began as an animal-first, organization-neutral way to make uncertainty visible in Chattanooga’s animal-control pathways. It aims to help residents find the next responsible step without pretending the evidence says more than it does.

It positions itself as a tool that:

• Makes uncertainty visible;
• Avoids overstating provider authority or outcomes;
• Uses fictional narratives to avoid real-world risks;
• Demonstrates how language models can be used responsibly in public services.

Inference: The project is positioned as a responsible AI prototype, emphasizing transparency and evidence-based decision-making over certainty or operational claims.

The description states that the tool is intended for Chattanooga residents who are uncertain about which animal-control provider to contact, due to:

• Municipal boundaries differing from provider roles;
• Mailing addresses not identifying governing jurisdiction;
• Official sources disagreeing.

It does not state a specific customer segment beyond this general audience. The system is described as organization-neutral, meaning it is not tied to any one agency or service provider.

Inference: The target customer is Chattanooga residents seeking clarity in animal-control pathways, with no explicit commercial or B2B focus.

The description does not mention a business model, pricing, or monetization strategy. It is described as a prototype built for a hackathon and is not intended to be operational or scalable beyond its demonstration.

Not evidenced: No information on revenue, pricing, or commercial viability.

The system uses:

• HTML5, CSS3, JavaScript for frontend;
• Python with loopback-only server;
• OpenAI GPT-5.6 via Responses API;
• Strict schema-constrained extraction;
• Deterministic routing gates;
• Evidence-preserving receipts;
• Accessibility features (keyboard, screen readers, dyslexia-friendly fonts, etc.).

It is described as:

• Dependency-free;
• Browser-based with no API key exposure;
• Using store: false for model requests;
• Executable tests included;
• Demonstrated with timed captions and transcripts.

Inference: The technical design emphasizes security, accessibility, and evidence transparency, with a clear separation between language understanding and authority assignment.

The project is described as a hackathon prototype. It does not claim any real-world deployment, user base, or traction beyond its own demonstration.

Not evidenced: No data on adoption, usage, revenue, or customer feedback.

The description does not mention competitors or similar tools in the animal welfare or public service routing space. It is a self-contained prototype, not positioned as part of an existing ecosystem.

Not evidenced: No competitive landscape or market positioning beyond its own claims.

• The system is fictional and non-operational, with no real-world data or users.
• It uses GPT-5.6, which may not be available in production environments, and the use of "store: false" suggests a limited or experimental setup.
• The project is described as a hackathon prototype — there is no indication it has evolved into a product or service.
• The system does not collect or store real data, but its design choices may not scale to a production environment without additional validation.

Inference: This is an experimental tool, not a commercial product. It lacks evidence of scalability, production readiness, or real-world impact.

1. What specific “reviewed routing research” underpins the three routing outcomes? Is it based on public records or internal documentation?
2. How does the deterministic gate ensure that only reviewed claims are used in routing decisions?
3. Has this system been tested with real animal reports, or is it limited to fictional examples?
4. What would be required to transition from a prototype to an operational system?
5. Are there plans to expand beyond Chattanooga, and if so, how would jurisdictional mapping scale?