EvidenceOps

Inspiration

Large language models can generate impressive answers, but they may also produce confident responses without sufficient evidence.

In many real-world situations, the challenge is not generating more information — it is understanding what information is actually supported, what is uncertain, and what still needs verification.

EvidenceOps was created to help people evaluate information quality before making important decisions.

What it does

EvidenceOps is an AI-powered evidence audit system that separates information into:

• Confirmed facts
• Assumptions and interpretations
• Unknowns and missing evidence
• Potential conflicts

Instead of immediately generating conclusions, EvidenceOps creates an audit-style view that helps users understand the reliability and limitations of the available information.

How we built it

We built EvidenceOps using OpenAI models and Codex as an AI-assisted development workflow.

The system analyzes user-provided information through a structured process:

1. Extract key claims.
2. Classify claims by evidence status.
3. Identify unsupported assumptions.
4. Generate a transparent evidence audit report.

Challenges we faced

The biggest challenge was designing an AI workflow that improves decision quality without creating false confidence.

We focused on making uncertainty visible and helping users understand the boundaries of AI-generated information.

Accomplishments

We created a working prototype that demonstrates a different approach to AI assistance: not only providing answers, but also showing the evidence, uncertainty, and reasoning boundaries behind those answers.

What we learned

AI becomes more trustworthy when it can communicate not only what it knows, but also what it does not know.

Built With

• api
• codex
• gpt
• javascript
• openai
• python
• react