Helping teams build consequential AI people can use, understand, and appropriately rely on.

Before people interact with your AI, include them first.

A technically correct AI response does not always lead to a successful human interaction.

People may misunderstand an answer, miss an important limitation, follow a recommendation without appropriate verification, abandon a task, or struggle to recognize when human support is needed.

QuorLoop helps teams evaluate this gap between model performance and human experience.

We are building an evaluation platform and approach that combines scenario-based testing, simulation where useful, and structured evaluation with relevant people. We connect findings to product changes and help teams test whether those changes improve the experience.

Our focus is consequential, human-facing AI: products whose interactions can meaningfully affect people's decisions, opportunities, or access to services.

We examine specific interactions across realistic tasks, user contexts, and conditions.

Evaluation questions include:

• Can people understand the response and its limitations?
• Can they identify and complete an appropriate next step?
• Do they recognize when to verify information or seek human support?
• Where does the interaction create confusion or misplaced reliance?
• What causes task abandonment or unintended action?
• Does a proposed product change improve the tested experience?

The unit of evaluation is the human–AI interaction—not just the model's answer.

1. Define the scope
   Identify the AI feature or workflow, intended users, relevant contexts, and questions the evaluation needs to answer.

2. Develop realistic scenarios
   Build tasks and conditions that probe the interaction, including consequential edge cases.

3. Examine product behavior
   Evaluate how the AI responds within the agreed scope. Use simulation where useful to explore scenarios and potential failure points.

4. Evaluate with relevant people
   Use structured tasks to examine how participants understand, respond to, and act on the experience.

5. Connect findings to improvements
   Identify changes to prompts, output design, guardrails, workflows, or escalation paths.

6. Re-test selected changes
   Assess whether changes improve the interaction and document what remains unresolved.

We distinguish between:

• Simulated behavior and observed participant behavior.
• Observations and interpretations.
• Potential failure points and demonstrated problems.
• Proposed improvements and improvements supported by follow-up testing.

Simulation does not replace evaluation with relevant people. A scoped evaluation does not establish that a product is safe, reliable, or effective for every person or context.

QuorLoop is for teams developing human-facing AI assistants, copilots, agents, and decision-support products.

We are especially interested in interactions that shape access to:

• Healthcare.
• Public benefits.
• Legal services.
• Education.
• Work.

The starting point is a concrete interaction and a question—not a blanket claim about an entire product.

As part of this work, we are building a paid community AI evaluator network across New York City.

We train everyday people to contribute their perspectives through structured evaluation tasks.

The network supports the evaluation process. Participant access alone is not the product: the work includes defining questions, designing tasks, analyzing evidence, recommending changes, and assessing whether those changes help.

This organization is the home for QuorLoop's development work and any resources we choose to publish.

Each public repository should describe its purpose, development status, setup requirements, limitations, and license.

Do not assume that material is open source or available for unrestricted reuse; consult the license and documentation in the relevant repository.

We welcome conversations with:

• AI product and engineering teams exploring a scoped evaluation.
• Researchers working on human–AI interaction and evaluation methods.
• Community organizations interested in the NYC evaluator network.
• Contributors interested in resources explicitly opened for collaboration.

For repository-specific questions or contributions, follow that repository's documentation and contribution guidelines.

If you are building consequential, human-facing AI and need to understand where people struggle—and whether your changes help—let's connect.

• Website : https://quorloop.com/evaluate
• LinkedIn : https://www.linkedin.com/company/quorloop
• Email : hello@quorloop.com

Status: In development.