Common Ground

The description states that Common Ground is a civic technology tool designed to help residents report local issues (e.g., blocked ramps, unsafe bus stops) and aggregate these reports into structured civic briefs. It follows a three-step loop:

1. Report: A resident privately records what happened, where, and when.
2. Confirm: Other people confirm only what they directly experienced, with dated evidence.
3. Act: Once credible patterns emerge, it produces a concise civic brief containing facts, uncertainty, public impact, and one measurable requested action.

The system is built using:

• Cloudflare Workers
• Codex
• GPT-5.6 (used for structuring inputs and guiding language, but not for autonomous decision-making)
• HTML5, CSS3, JavaScript

It includes an interactive demo with three realistic civic cases that users can explore.

Inference The product is a prototype built to demonstrate how AI might be used in a controlled, privacy-preserving way to aggregate citizen concerns into actionable civic data. It is not a commercial SaaS offering.

The description states that Common Ground:

• Turns local concerns into safer civic action.
• Is “stress-tested for privacy, clarity, and human review.”
• Is deliberately not a petition platform or popularity contest.
• Aims to create the “missing middle” between individual reports and institutional response.

It positions itself as a tool that:

• Rewards independent corroboration over virality.
• Preserves identity while strengthening collective evidence.
• Uses AI not for judgment, but for translation and constraint.

Inference The positioning is centered on privacy-preserving civic engagement, using AI to structure rather than automate decisions. It emphasizes credibility and human agency in the process of reporting and acting on public concerns.

The description states that Common Ground targets:

• Residents who experience local civic issues.
• Public bodies (institutions) that need structured, credible data to act on.

It also mentions a planned pilot with:

• One municipality
• One disability-access organisation

Inference The primary user base is likely citizens reporting problems, and the secondary audience is local government or advocacy organisations. The ICP appears to be civic actors who value structured, verifiable data over emotional or viral signals.

The description does not state:

• Any pricing model
• Revenue streams
• Commercial partnerships
• Subscription or usage fees

It only mentions that the project is a prototype, built for a hackathon.

Inference There is no evidence of a business model. The project appears to be in early development and has no commercial traction or monetisation strategy described.

The system uses:

• Codex and GPT-5.6 for structuring inputs, shaping language, and guiding interaction.
• A server-side Responses API contract that converts messy reports into structured outputs.
• Deterministic fallbacks to avoid exposing API keys in the browser.
• Cloudflare Workers, HTML5, CSS3, JavaScript.

The system is designed with:

• Explicit privacy boundaries
• Provenance tracking
• A bounded AI role — not for autonomous judgment or publishing claims

Inference The technical architecture shows a deliberate effort to use AI responsibly and securely. It avoids exposing sensitive data and uses structured outputs to guide user behavior.

The description states:

• It is an interactive prototype
• It was built for the OpenAI 2026 hackathon
• It includes three realistic civic cases in the demo
• It has a visible Report → Confirm → Act journey

It does not mention:

• Any live users or customers
• Revenue or funding
• Product adoption metrics
• Institutional partnerships

Inference The project is at an early stage of development. There is no evidence of real-world usage, traction, or institutional engagement.

The description does not reference any competitors or similar tools.

Inference No competitive landscape is described. It is unclear whether Common Ground is positioned against existing civic platforms, petition sites, or government feedback systems.

• No commercial traction or revenue model: The project is a prototype with no evidence of monetisation.
• Limited team size (1 person): This raises questions about scalability and execution capacity.
• AI dependency without clear governance: While AI is used for structuring, the lack of clarity on how it's regulated or audited may be a concern.
• No institutional adoption yet: The pilot plans are not yet executed.
• Self-reported only: All claims are unverified.

1. What is the expected timeline for moving from prototype to a functional product?
2. How do you plan to validate that the system doesn’t amplify harm or bias in its structuring of reports?
3. What are the key challenges in scaling this beyond a single hackathon project?
4. Are there any early institutional partners or pilot users already engaged?
5. How do you intend to ensure long-term sustainability and avoid over-reliance on AI for decision-making?